Schema management is a basic problem in many database application domains such as data integration systems. Users need to access and manipulate data from several databases. In this context, in order to integrate data from distributed heterogeneous database sources, data integration systems demand the resolution of several issues that arise in managing schemas. In this paper, we present a brief survey of the problem of schema matching which is used for solving problems of schema integration processing. Moreover, we propose a technique for integrating and querying distributed heterogeneous XML schemas.

This paper describes an approach for mediation of heterogeneous XML schemas. Such an approach is proposed as a tool for XML data integration system. A global XML schema is specified by the designer to provide a homogeneous view over heterogeneous XML data. An XML mediation layer is introduced to manage: (1) establishing appropriate mappings between the global schema and the schemas of the sources; (2) querying XML data sources in terms of the global schema. The XML data sources are described by XML Schema language. The former task is performed through a semi-automatic process that generates local and global paths. A tree structure for each XML schema is constructed and represented by a simple form. This is in turn used for assigning indices manually to match local paths to corresponding global paths. By gathering all paths with the same indices, the equivalent local and global paths are grouped automatically, and an XML Metadata Document is constructed. An XML Query Translator for the latter task is described to translate a global user query into local queries by using the mappings that are defined in the XML Metadata Document.

This paper describes an approach for mediation of heterogeneous XML schemas. Such an approach is proposed as a tool for XML data integration system. A global XML schema is specified by the designer to provide a homogeneous view over heterogeneous XML data. An XML mediation layer is introduced to manage: (1) establishing appropriate mappings between the global schema and the schemas of the sources; (2) querying XML data sources in terms of the global schema. The XML data sources are described by XML Schema language. The former task is performed through a semi-automatic process that generates local and global paths. A tree structure for each XML schema is constructed and represented by a simple form. This is in turn used for assigning indices manually to match local paths to corresponding global paths. By gathering all paths with the same indices, the equivalent local and global paths are grouped automatically, and an XML Metadata Document is constructed. An XML Query Translator for the latter task is described to translate a global user query into local queries by using the mappings that are defined in the XML Metadata Document.

Much research work has recently focused on the problem of representing historical information in XML. This report describes a number of temporal XML data models and provides their comparison according to the following properties: time dimension (valid time, transaction time), support of temporal elements and attributes, querying possibilities, association to XML Schema/DTD, and influence on XML syntax.

Much research work has recently focused on the problem of representing historical information in XML. In this paper, we describe an ongoing work to represent XML changes. Our model is a three-dimensional XML-based model (3D_XML in short) for representing and querying histories of XML documents. The proposed model incorporates three time dimensions, valid time, transaction time, and efficacy time without extending the syntax of XML. We use XQuery to express complex temporal queries on the evolution of the document contents. We believe that native XML databases (NXDs) present a viable alternative to relational temporal databases when complex time dependent data has to be manipulated and stored. So NXDs will be our choice.

Discovering the complex relationships between entities is one way of benefitting from the Semantic Web. This paper discusses new approaches to implementing rho-operators into RDF querying engines which will enable discovering such relationships viable. The cornerstone of such implementation is creating an index which describes the original RDF graoh. The index is created by recursive application of a transformation of graph to forest of trees and then to each tree its extended signature is created. The signatures are accompanied by the additional information about transformed problematic nodes breaking the tree structure. The components described by the signatures are assumed as a single node in the following step. The transitions between the signatures represent edges.

The effort described in this paper introduces an indexing structure for path search in the graph structured data called rho-index. It is based on a graph segmentation S(G) that is meant to represent the indexed graph G in a simpler manor yet having similar properties as the graph G had. This is achieved using graph transformations and a special type of a matrix used to represent the transformed graph.

In this paper a correlation between rho-operators and indirect relations in citation analysis is presented. The rho-operators were defined to explore complex relationships in graph structured data. Various direct and indirect relations identified in citation analysis are used to study the semantics within the citation network. The rho-index was used to implement the rho - path search in the citation network and gained results are evaluated and discussed.

In this paper, we tackle the issues of analyzing the struc- tural evolution of the metric social network. The metric social network operates in a P2P environment where peers maintain their own data and the relationships among them are formed on the basis of the pro- cessed similarity queries. The evolution is analyzed by traditional social networking tools the characteristic path length and the clustering co- efficient. Nonetheless, due to the special structure of the metric social network, own designed gauges the average overlap and robustness of description coefficients are presented to analyze the structure of emerg- ing communities encompassing similar data.

Since its introduction in 1997, the M-tree became a respected metric access method (MAM), while remaining, together with its descendants, still the only database-friendly MAM, that is, a dynamic structure persistent in paged index. Although there have been many other MAMs developed over the last decade, most of them require either static or expensive indexing. By contrast, the dynamic M-tree construction allows us to index very large databases in subquadratic time, and simultaneously the index can be maintained up-to-date (i.e., supports arbitrary insertions/deletions). In this article we propose two new techniques improving dynamic insertions in M-tree—the forced reinsertion strategies and so-called hybrid-way leaf selection. Both of the techniques preserve logarithmic asymptotic complexity of a single insertion, while they aim to produce more compact M-tree hierarchies (which leads to faster query processing). In particular, the former technique reuses the well-known principle of forced reinsertions, where the new insertion algorithm tries to re-insert the content of an M-tree leaf that is about to split in order to avoid that split. The latter technique constitutes an efficiency-scalable selection of suitable leaf node wherein a new object has to be inserted. In the experiments we show that the proposed techniques bring a clear improvement (speeding up both indexing and query processing) and also provide a tuning tool for indexing vs. querying efficiency trade-off. Moreover, a combination of the new techniques exhibits a synergic effect resulting in the best strategy for dynamic M-tree construction proposed so far.

Due to the increasing complexity of current digital data, similarity search has become a fundamental computational task in many applications. Unfortunately, its costs are still high and the linear scalability of single server implementations prevents from efficient searching in large data volumes. In this paper, we shortly describe four recent scalable distributed similarity search techniques and study their performance of executing queries on three different datasets. Though all the methods employ parallelism to speed up query execution, different advantages for different objectives have been identified by experiments. The reported results can be exploited for choosing the best implementations for specific applications. They can also be used for designing new and better indexing structures in the future.

The similarity search has become a fundamental computational task in many applications. One of the mathematical models of the similarity the metric space has drawn attention of many researchers resulting in several sophisticated metric-indexing techniques. An important part of a research in this area is typically a prototype implementation and subsequent experimental evaluation of the proposed data structure. This paper describes an implementation framework called MESSIF that eases the task of building such prototypes. It provides a number of modules from basic storage management to automatic collecting of performance statistics. Due to its open and modular design it is also easy to implement additional modules if necessary. The MESSIF also offers several ready-to-use generic clients that allow to control and test the index structures and also measure its performance.

Since its introduction in 1997, the M-tree became a respected metric access method (MAM), while remaining, together with its descendants, still the only database-friendly MAM, that is, a dynamic structure persistent in paged index. Although there have been many other MAMs developed over the last decade, most of them require either static or expensive indexing. By contrast, the dynamic M-tree construction allows us to index very large databases in subquadratic time, and simultaneously the index can be maintained up-to-date (i.e., supports arbitrary insertions/deletions). In this article we propose two new techniques improving dynamic insertions in M-tree—the forced reinsertion strategies and so-called hybrid-way leaf selection. Both of the techniques preserve logarithmic asymptotic complexity of a single insertion, while they aim to produce more compact M-tree hierarchies (which leads to faster query processing). In particular, the former technique reuses the well-known principle of forced reinsertions, where the new insertion algorithm tries to re-insert the content of an M-tree leaf that is about to split in order to avoid that split. The latter technique constitutes an efficiency-scalable selection of suitable leaf node wherein a new object has to be inserted. In the experiments we show that the proposed techniques bring a clear improvement (speeding up both indexing and query processing) and also provide a tuning tool for indexing vs. querying efficiency trade-off. Moreover, a combination of the new techniques exhibits a synergic effect resulting in the best strategy for dynamic M-tree construction proposed so far.

One of the areas of data integration covers systems that maintain co-herence among a heterogeneous set of databases. Such a system repeatedly col-lects data from the local databases, synchronizes them, and pushes the updates back. One of the key problems in this architecture is the conflict resolution. When data in a less relevant data source changes, it should not cause any data change in a store with higher relevancy. To meet such requirements, we propose a DataPile structure with following main advantages: effective storage of historical versions of data, straightfor-ward adaptation to global schema changes, separation of data conversion and replication logic, simple implementation of data relevance. Key usage of such mechanisms is in projects with following traits or require-ments: integration of heterogeneous data from sources with different reliability, data coherence of databases whose schema differs, data changes are performed on local databases and minimal load on the central database.

A formalism for the logical description of computational agents and multi-agent systems is given. It is explained how it such a formal description can be used to configure and reason about multi-agent systems realizing computational intelligence models. A usage within a real software system Bang 3 is demonstrated. The logical description of multiagent systems opens Bang 3 for interaction with ontology based distributed knowledge systems like the Semantic Web or the Knowledge Grid.

In this article, the author generalizes Dempster`s rule, Yager`s rule, and Dubois-Prade`s rule for belief functions combination in order to be applicable to hyper-power sets according to the Dezert-Smarandache (DSm) Theory. A comparison of the rules with the DSm rule of combination is further presented.

This contribution deals with a belief processing which enables managing of multiple and overlapping elements of a frame of discernment. An outline of the Dempster-Shafer theory for such cases is presented, including several types of constraints for simplification of its large computational complexity. DSmT - a new theory rapidly developing the last five years - is briefly introduced. Finally, it is shown that the DSmT is a special case of the general Dempster-Shafer approach.

Definitions of two different ways of coarsening of basic belief assignments to opinions the simple coarsening and the normal coarsening are recalled in this contribution. A relation of results of combination of the normal opinions using the consensus operator to belief functions on an original n-element frame of discernment is examined. A questionable meaning of the normal coarsening is discussed.

In this study we engage in belief processing which enables managing of multiple and overlapping elements of a frame of discernment. Our focus is directed on DSm approach, which was originally introduced as a generalization of the Dempster-Shafer theory. Paradoxically, later it was presented also as a special case of the Dempster-Shafer approach applied on such frame of discernment. In this paper we discuss what is new in DSm approach, what are the benefits of DSm approach, and what is its real contribution to belief function theory.

Principal ideas of the minC combination are recalled. A mathematical structure of generalized frames of discernment is formalized. A generalized schema for a computation of the minC combination is presented. Conflicting belief masses redistribution among non-conflicting focal elements is overviewed. Final general formulas for computation of the minC combination are presented. Some examples of computation of the minC combination follow. Related works and approaches are referred in the end.

V tomto příspěvku se zbýváme extrakcí informací z webových zdrojů převážně textového charakteru. K tomuto účelu jsme se pokusili využít několik lingvistických nástrojů pro zpracování přirozeného textu v češtině. Jmenovitě se jedná o nástroje pražského projektu PDT a český WordNet. Cílem příspěvku je přiblížit možnosti, které tyto nástroje pro extrakci informací z textu poskytují. Extrakcí informací se zde zabýváme především v kontextu sémantického webu a zkoumáme možnosti, jak tyto nástroje využít pro automatizaci sémantické anotace stránek současného webu.

The metric space paradigm has recently received attention as an important model of similarity in the area of Bioinformatics. Numerous techniques have been proposed to solve similarity (range or nearest-neighbor) queries on collections of data from metric domains. Though important representatives are outlined, this chapter is not trying to substitute existing comprehensive surveys. The main objective is to explain and prove by experiments that similarity searching is typically an expensive process which does not easily scale to very large volumes of data, thus distributed architectures able to exploit parallelism must be employed. After a review of applications using the metric space approach in the field of Bioinformatics, the chapter provides an overview of methods used for creating index structures able to speedup retrieval. In the metric space approach, only pair-wise distances between objects are quantified, so they represent the level of dissimilarity. The key idea of index structures is to partition the data into subsets so that queries are evaluated without examining entire collections -- minimizing both the number of distance computations and the number of I/O accesses. These objectives are obtained by exploiting the property of metric spaces called the triangle inequality which states that if two objects are near a third object, they cannot be too distant to one another. Unfortunately, computational costs are still high and the linear scalability of single-computer implementations prevents from searching in large and ever growing data files efficiently. For these reasons, we describe very recent parallel and distributed similarity search techniques and study performance of their implementations. Specifically, Section 12.1 presents the metric space approach and its applications in the field of Bioinformatics. Section 12.2 describes some of the most popular centralized disk-based metric indexes. Consequently, Section 12.3 concentrates on parallel and distributed access methods which can deal with data collections that for practical purposes can be arbitrary large, which is typical for Bioinformatics workloads. An experimental evaluation of the presented distributed approaches on real-life data sets is presented in 12.4. The chapter concludes in Section 12.5.

Due to the exponential growth of digital data and its complexity, we need a technique which allows us to search such collections efficiently. A suitable solution is based on the peer-to-peer (P2P) network paradigm and the metric-space model of similarity. When a large volume of data is being inserted, the P2P network must expand to new peers in order to maintain its efficiency. Thus, many peers must be split. During a peer split, the data is halved and one half is migrated to a new peer. In this paper, we study the problem of peer splits and propose a specialized algorithm for speeding it up. In particular, we use the structured P2P network called the M-Chord. Search performance within a single peer is enhanced by the M-tree. In experimental evaluation, we compare the proposed algorithm with several straightforward solutions on a real network organizing 10 million images. Our algorithm provides a significant performance boost.

Idea sémantického webu je široce diskutována mezi odbornou veřejností již mnoho let. Přestože je vyvinuta řada technologií, jazyků, prostředků a dokonce i softwarových nástrojú, málokdo někdy nějaký reálný sémantický web viděl. Za jeden z hlavních dùvodù tohoto stavu považujeme neexistenci potřebné infrastruktury pro provoz sémantického webu. V našem článku popisujeme návrh takové infrastruktury, která je založena na využití a rozšíření technologie datového stohu a nástrojích pro něj vyvinutých a jejich kombinaci s webovými vyhledávači a dalšími nástroji a prostředky.

Basic storage and querying of RDF data using a relational database can be done in a very simple manner. Such approach can run into trouble when used on large and complex data. This paper presents such data and several sample queries together with analysis of their performance. It also describes two possible ways of improving the performance based on this analysis.

The Semantic Web is not widespread as it has been expected by its founders. This is partially caused by lack of standard and working infrastructure for the Semantic Web. We have built a working, portable, stable, highperformance infrastructure for the Semantic Web. This paper is focused on tasks performed by the infrastructure.

The Semantic Web is not widespread as it has been expected by its founders. This is partially caused by lack of standard and working infrastructure for the Semantic Web. We have built a working, portable, stable, high-performance infrastructure for the Semantic Web. This enables various experiments with the Semantic Web in the real world.

Since many XML documents do not contain any schema definition, we expected that there will be also RDF documents without RDF schema or ontology.Then the data can only be viewed as a general labeled directed graph and the idea to present the data to the user by drawing the graph seems natural. Because the data can be extremely large, it is impossible to display the whole graph at one time. Only a suitable start node is displayed and the rest of the graph can be explored by incremental navigation.To conserve space and show possible directions of further navigation to the user we have come up with a technique called node merging. By combining suitable graph drawing and navigation techniques we get a tool that can give the user good idea about structure and content of the data.

We have developed and implemented [1,2] infrastructure and RDF storage for the Semantic Web. When we filled it with data the need for some tool that could explore the data became evident. Unfortunately, none of existing solutions fulfills requirements imposed by the data and users expectations. This paper presents our RDF visualizer that was designed specifically to handle large RDF data by means of incremental navigation. A detailed description of the algorithm is given as well as actual results produced by the visualizer.

Uncertainty querying of large data can be solved by providing top-k answers according to a user fuzzy ranking/scoring function. Usually different users have different fuzzy scoring function a user preference model. Main goal of this paper is to assign a user a preference model automatically. To achieve this we decompose user’s fuzzy ranking function to ordering of particular attributes and to a combination function. To solve the problem of automatic assignment of user model we design two algorithms, one for learning user preference on particular attribute and second for learning the combination function. Methods were integrated into a Fagin-like top-k querying system with some new heuristics and tested.

User preferences became recently a hot topic. The massive use of internet shops and social webs require the presence of a user modelling, which helps users to orient them selfs on a page. There are many different approaches to model user preferences. In this paper, we will overview the current state-of-the-art in the area of acquisition of user preferences and their induction. Main focus will be on the models of user preferences and on the induction of these models, but also the process of extracting preferences from the user behaviour will be studied. We will also present our contribution to the probabilistic user models.

We discuss models of user and group preferences in social networks and the Semantic web. We construct a model for user and group preference querying over RDF data as well as for ordering of answers by aggregation of particular attribute ranking. We have implemented our methods and heuristics into the Tokaf middleware framework prototype. We describe also experiments with Tokaf.

We discuss models of user preferences in Web environment. We construct a model for user preference querying over a number of data sources and ordering of answers by a combination of particular attribute rankings. We generalize Fagin's algorithm in two directions - we develop some new heuristics for top-k search in the model without random access and propose a method of ordering lists of objects by user fuzzy function. To enable different user preferences our system does not require objects to be sorted - instead we use a B+- tree on each of the attribute domains. This leads to a more realistic model of Web services. We implement our methods and heuristics for search of top-k answers into Tokaf middleware framework prototype. We describe experiments with Tokaf and compare different performance measures with some other methods.

Web search heuristics based on Fagin’s threshold algorithm assume we have the user profile in the form of particular attribute ordering and a fuzzy aggregation function representing the user combining function. Having these, there are sufficient algorithms for searching top-k answers. Finding particular attribute ordering and aggregation for a user still remains a problem. In this short paper our main contribution is a proof of concept of a new iterative process of acquisition of user preferences and attribute ordering .

During the implementation of an environmental accounting system in a company, one of the most important pieces of information to obtain is a detailed understanding about material flows (raw materials, semi-finished products, final products and wastes) and flows of different types of energy inputs (buying, selling and wastage) when thinking about the consequences on the company. The authors, in the article: 1) study the question of the quantification of the flows, and the accuracy of their measurement, 2) provide an environmental accounting statement, with help of standards and indices, statistical trends analysis.

A common problem encountered in disciplines such as statistics, data analysis, signal processing, textual data representation, and neural network research, is finding a suitable representation of the data in the lower dimension space. One of the principles used for this reason is a factor analysis. In this paper, we show that Hebbian learning and a Hopfield-like neural network could be used for a natural procedure for Boolean factor analysis. To ensure efficient Boolean factor analysis, we propose our original modification not only of Hopfield network architecture but also its dynamics as well. In this paper, we describe neural network implementation of the Boolean factor analysis method. We show the advantages of our Hopfield-like network modification step by step on artificially generated data. At the end, we show the efficiency of the method on artificial data containing a known list of factors. Our approach has the advantage of being able to analyze very large data sets while preserving the nature of the data.

This paper presents a method for dynamization which may be used for fast and effective inverted index maintenance. Experimental results show that the dynamization process is possible and that it guarantees the response time for the query operation and index actualization.

EGOTHOR is a search engine that indexes the Web and allows us to search the Web documents. Its hit list contains URL and title of the hits, and also some snippet which tries to shortly show a match. The snippet can be almost always assembled by an algorithm that has a full knowledge of the original document (mostly HTML page). It implies that the search engine is required to store the full text of the documents as a part of the index. Such a requirement leads us to pick up an appropriate compression algorithm which would reduce the space demand. One of the solutions could be some use of common compression methods, for instance gzip or bzip2, but it might be preferable to develop a new method which would take advantage of the document structure, or rather, the textual character of the documents. There already exist special compression text algorithms and methods for a compression of XML documents. The aim of this paper is an integration of the two approaches to achieve an optimal level of the compression ratio.

We propose a model of a middleware system enabling personalized web search for users with different preferences. We integrate both inductive and deductive tasks to find user preferences and consequently best objects. The model is based on modeling preferences by fuzzy sets and fuzzy logic. We present the model-theoretic semantic for fuzzy description logic f-EL which is the motivation of creating a model for fuzzy RDF. Our model was experimentally implemented and integration was tested.

A version of fuzzy description logic based on the basic (continuous t-norm based) fuzzy predicate logic BL is presented. Problems of satisfiability, validity and subsumption of concepts are discussed and reduced to problems of fuzzy propositional logic known to be decidable for any continuous t-norm. For Lukasiewicz t-norm some stronger results are obtained.

Continuous t-norm based fuzzy predicate logic is surveyed as a generalization of classical predicate logic; then a kind of fuzzy description logic based on our fuzzy predicate logic is briefly described as a powerful but still decidable formal system of description logic dealing with vague (imprecise) concepts.

This paper reports a new kind of lexicon currently being developed as a resource for natural language processing, language teaching, and other applications. This is a "Pattern Dictionary of English", based on detailed and extensive corpus analysis of each sense of each verb in the language. A pattern consists of a verb with its valencies, plus semantic values for each valency and other relevant clues, and is associated with an implicature that associates the meaning with the context rather than with the word in isolation. For each verb, all normal patterns are recorded. The semantic types in each argument slot are linked to actual words via a large ontology. The dictionary is aimed primarily at the NLP community, but it also has relevance for language teaching. For NLP purposes, matching actual uses of verbs in previously unseen text to patterns in the pattern dictionary offers some hope of solving the “Word Sense Disambiguation (WSD) problem”. The paper discusses the relationship between A) words as they are actually used and B) semantic types and functions in a theoretical lexicon. An attempt will be made in the full paper to relate empirically observable, corpus-based facts about ordinary word use to the theoretical abstractions of Generative Lexicon Theory of James Pustejovsky. Lexicography and linguistic theory are often uneasy bedfellows, but I shall suggest that in at least these two cases, there is a possibility of a harmonious and productive relationship.

This paper examines some issues involved in building a corpus-based ontology for use in determining the meaning of words in text, in the context of creating a “pattern dictionary”. How do words cluster in paradigmatic lexical sets in actual usage (as reflected in a large corpus), and can these clusters be mapped onto a semantically structured ontology? What semantic notions need to be distinguished for this purpose, and what are the appropriate theoretical foundations? What other elements are needed for the application of determining meaning in text?

In the paper we deal with the derivational relations in Czech that form typical derivational nests (or subnets). Derivational relations are mostly of semantic nature and their regularity in Czech allows us to describe them in a way suitable for computer processing and then add them to the electronic databases such as WordNet almost automatically. For this purpose we have used the derivational version of morphological analyzer Ajka that is able to handle the basic and most productive derivational relations in Czech. A special derivational interface has been developed in our NLP Lab at FI MU by means of which we have explored the semantic nature of the selected noun derivational suffixes (22) as well as verb prefixes and established a set of the semantically labeled derivational relations, presently 14. With regard to the verbs we have paid attention to the selected verb semantic classes in connection with the derivational relations between selected prefixes (4) and corresponding Czech verbs. As an application we have added the selected derivational relations to the Czech WordNet and in this way enriched it with approx. 30 000 new Czech synsets.

Příspěvek se zabývá problematikou získávání logických tvrzení z dat, tedy těmi metodami dobývání znalostí z dat (data mining), jejichž výsledky lze vyjádřit v jazyce nějaké formální logiky. Je podán velmi stručný přehled širokého spektra rozmanitých metod tohoto typu, jak metod vycházejících ze statistických přístupů, tak i metod spočívajících na principech strojového učení, a je poukázáno na specifický charakter metod založených na umělých neuronových sítích. Pro ilustraci jsou podrobněji popsány dvě konkrétní metody získávání logických tvrzení z dat. Jednou z nich je metoda Guha, která vychází z observační logiky a je pravděpodobně nejstarší metodou získávání pravidel z dat vůbec. Druhou je metoda založená na po částech lineárních vícevrstvých perceptronech.

In this paper, we present the new wordnet development tool called DEBVisDic. It is built on the recently developed platform for clientserver XML databases, called DEBii. This platform is able to cover many possible applications, from which we concentrate on the new, complete reimplementation of one of the mostspread wordnet editor and browser – VisDic. We argue for the benefits the new DEBii platform brings to wordnet editing and to XML databases in general. In the paper, we describe the state of the implementation, the insides and interfaces of the DEBVisDic tool. We also discuss its functionality and some distinctions in comparison with other dictionary writing systems.

This paper presents a new implementation of administration framework for the DEBII dictionary writing system. We present the details and examples of the user management part as well as graphical scenarios for dictionary service setup, adaptation and automatic generation of user application based on the dictionary XML schema. Tento článek představuje novou implementaci administračního rozhraní systému pro tvorbu slovníků DEBII. V článku je podrobně popsán systém správy uživatelů a také grafikou doplněný postup vytvoření nového slovníku, jeho přizpůsobení a automatické generování uživatelské aplikace pomocí XML schématu slovníku.

In this paper, we summarize the latest development regarding the client dictionary writing applications based on the DEB development platform. The DEB framework is nowadays used in several full grown projects for preparation of high quality lexicographic data created within (possibly distant) teams of researchers. We briefly present the current list of DEB applications with the relevant projects and their phases. For each of the applications, we offer display the view of the interface with overview description of the most important features.

In the paper, we introduce new dictionary management interface for design, preparation and presentation of generic electronic XML dictionaries using the DEB (Dictionary Editing and Browsing) development platform. The DEB platform provides a strict client-server environment for general dictionary writing systems. So far several successful NLP tools have been implemented on this platform, one of the most known being the DEBVisDic tool for wordnet semantic network editing and visualization. This paper describes a new part of the DEB platform -- the Administration interface that is shared by all DEB applications running on one server machine. Článek představuje nové rozhraní pro správu slovníků, které umožňuje návrh, přípravu a prezentaci obecných elektronických slovníků ve formátu XML s použitím vývojové platformy DEB (Dictionary Editing and Browsing). Platforma DEB poskytuje prostředí v architektuře klient-server pro obecné systémy pro vytváření slovníků. V současné době bylo na této platformě implementováno několik úspěšných NLP nástrojů, nejznámnější je nástroj DEBVisDic pro editaci a vizualizaci sémantických sítí typu wordnet. Tento článek popisuje novou součást platformy DEB -- administrační rozhraní, které sdílí všechny DEB aplikace spuštěné na jednom serveru.

In the paper, we present the results of an experiment with comparing the effectiveness of real text parsers of Czech language based on completely different approaches stochastic parsers that provide dependency trees as their outputs and a meta-grammar parser that generates a resulting chart structure representing a packed forest of phrasal derivation trees. We describe and formulate main questions and problems accompanying such experiment, try to offer answers to these questions and finally display also factual results of the tests measured on 10 thousand Czech sentences.

The chapter provides a survey of some clustering methods relevant to the clustering document collections and, in consequence, Web data. We start with classical methods of cluster analysis which seem to be relevant in approaching to cluster Web data. The graph clustering is also described since its methods contribute significantly to clustering Web data. A use of artificial neural networks for clustering has the same motivation. Based on previously presented material, the core of the chapter provides an overview of approaches to clustering in the Web environment. Particularly, we focus on clustering web search results, in which clustering search engines arrange the search results into groups around a common theme. We conclude with some general considerations concerning the justification of so many clustering algorithms and their application in the Web environment.

Most of information retrieval systems depend on Boolean queries. The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we deal with using of Genetic algorithms in Information retrieval specially in optimizing of a Boolean query.

In this paper, we compare performance of novel neural network based algorithm for Boolean factor analysis with several dimension reduction techniques as a tool for feature extraction. Compared are namely singular value decomposition, semi-discrete decomposition and non-negative matrix factorization algorithms, including some cluster analysis methods as well. Even if the mainly mentioned methods are linear, it is interesting to compare them with neural network based Boolean factor analysis, because they are well elaborated. Second reason for this is to show basic differences between Boolean and linear case. So called bars problem is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by these methods. Resulting images show that Boolean factor analysis is upmost suitable method for this kind of data.

After it has been successfully done in probability and possibility theories, the paper is the first attempt to introduce the operator of composition also for belief functions. We prove that the proposed definition preserves all the necessary properties of the operator enabling us to define compositional models as an efficient tool for multidimensional models representation.

In the paper, we present the beautified chart method used for reducing the number of output derivation trees for the Czech syntax parser synt. We show the evaluation results of the method, describe the appropriate algorithms and the parser internal data structures as well as problems with their implementation. Článek popisuje metodu beautified chart pro omezení počtu výstupních derivačních stromů syntaktického analyzátoru češtiny synt. Je popsána naměřená úspěšnost metody, příslušné algoritmy, datové struktury a některé problémy při implementaci.

Multi-agent systems typically utilize a non-blocking asynchronous communication in order to achieve required flexibility and adaptability. High performance computing techniques exploit the current hardware ability of overlapping asynchronous communication with computation to load the available computer resources efficiently. On the contrary, widely used parallel processes modeling methodologies do not often allow for an asynchronous communication description. At the same time those models do not allow their user to select the granularity level and provide only a fixed set of machine and algorithm description quantities. In this work we addressed this issue and designed a new parallel processes modeling methodology. Its main features include an open set of atomic operations that are calculated and predicted for the algorithm in question, and the computer aided semi-automatic measuring of operation counts and approximation of cost functions. This allows not only for tuning the model granularity as well as accuracy according to user needs, but also to reach a such description complexity that would be very difficult to obtain without any computer aid. We demonstrated that our approach gives good results on the parallel implementation of a selected generalized genetic algorithm. A model was constructed and its predictions compared with the reality on various computer architectures, including one parallel cluster machine. We also designed and implemented an open multi-agent system suitable for the above mentioned experiments and many others. This system synthesizes the areas of high performance computing, multi-agent systems and computational intelligence into an efficient and flexible means of running experiments.

This paper introduces a novel method for semantic annotation of web pages. Semantic annotation is performed with regard to unwritten and empirically proven agreement between users and web designers using Web patterns. This method is based on extraction of patterns which are characteristic for concrete domain. Patterns provide formalization of the agreement and allows assignment of semantics to parts of web pages. Experimental results verify the e®ectives of the proposed method.

We discuss two approaches to supervised learning, namely regularization networks and RBF networks, and demonstrate their performance on experiments. We claim that the performance of these two models is comparable, so the RBF networks can be used as a cheaper alternative to regularization networks.

Kernel based learning methods are subject of great interest at present. We discuss two kernel based learning methods, namely the Regularization Networks (RN) and the Radial Basis Function Network (RBF networks). The RNs are derived from the regularization theory, had been studied thoroughly from a function approximation point of view, and therefore have very good theoretical background. The RBF networks represent a model of artificial neural networks with both neuro-physiological and mathematical motivation. In addition they may be treated as a generalised form of Regularization Networks, i.e. RN with increased number of kernel functions. We demonstrated the performance of both approaches on experiments, including both benchmark and real-life learning tasks. We claim that the performance of RN and RBF network is comparable in terms of generalisation error. The RN approach usually leads to solutions with higher model complexity (high number of base units). In this situations, the RBF networks can be used as a ’cheaper’ alternative.

In this paper we study learning with Regularization Networks (RN). RN are feedforward neural networks with one hidden layer. Since they have a very good theoretical background, we study their practical aspects and applicability. On experiments we demonstrate the role of the regularization parameter, compare RN with different kernels and parameter settings on benchmark data sets. Then we apply RN to a problem of a flow rate prediction, real data from Czech river Sázava are used. All experiments were made using the system Bang.

The problem of supervised learning is a subject of great interest at present. It covers wide range of tasks, such as various classification, prediction, or forecasting problems, i.e. problems that also often arise in semantic web applications. We study one approach to this problem - regularization networks. We introduce composite types of kernel functions, sum kernels and product kernels. On experiments we demonstrate the role of the regularization parameter and kernel function in the regularization network learning, and compare networks with different types of kernel functions.

Learning from data with generalization capability is studied in the framework of minimization of regularized empirical error functionals over nested families of hypothesis sets with increasing model complexity. ForTikhonov`s regularization with kernel stabilizers, minimization over restricted hypothesis sets containing for a fixed integer n only linear combinations of all n-tuples of kernel functions is investigated. Upper bounds are derived on the rate of convergence of suboptimal solutions from such sets to the optimal solution achievable without restrictions on model complexity.The bounds are of the form 1/sqrt(n) multiplied by a term that depends on the size of the sample of empirical data, the vector of output data, the Gram matrix of the kernel with respect to the input data, and the regularization parameter.

It is shown that learning from data modelled as minimaization of error functionals can be reformulated in terms of inverse problems. This reformulation allows to characterize optimal input-output functions of networks with kernel units.

Complexity of data with respect to a particular class of neural networks is studied. Data complexity is measured by the magnitude of a certain norm of either the regression function induced by a probability measure describing the data or a function interpolating a sample ofinput/output pairs of training data chosen with respect to this probability. The norm is tailored to a type of computational units in the network class. It is shown that for data for which this norm is small, convergence of infima of error functionals over networks with increasing number of hidden units to the global minima is relatively fast. Thus for such data, networks with a reasonable model complexity can achieve good performance during learning. For perceptron networks, the relationship between data complexity, data dimensionality and smoothness is investigated.

Syllable based text compression is a new approach to compression by symbols. In this concept syllables are used as the compression symbols instead of the more common characters or words. This new technique has proven itself worthy especially on short to middle-length text files. The effectiveness of the compression is greatly affected by the quality of dictionaries of syllables characteristic for the certain language. These dictionaries are usually created with a straight-forward analysis of text corpora. In this paper we would like to introduce an other way of obtaining these dictionaries using genetic algorithm. We believe, that dictionaries built this way, may help us lower the compress ratio. We will measure this effect on a set of Czech and English texts.

The Burrows-Wheeler Transform (BWT) is a compression method which reorders an input string into the form, which is preferable to another compression. Usually Move-To-Front transform and then Huffman coding is used to the permutated string. The original method [3] from 1994 was designed for an alphabet compression. In 2001, versions working with word and n-grams alphabet were presented. The newest version copes with the syllable alphabet [7]. The goal of this article is to compare the BWT compression working with alphabet of letters, syllables, words, 3-grams and 5-grams.

Integration has been an acknowledged data processing problem for a long time. However, there is no universal tool for general data integration. Because various data descriptions, data heterogeneity, and machine unreadability, it is not easy way. Improvement in this situation could bring the Semantic Web. Its idea is based on machine understandable web data, which bring us an opportunity of better automated processing. The SemanticWeb is still a future vision, but there are already some features we can use. The paper describes how is integration solved in mediation integration system VirGIS and discusses use of nowadays Semantic Web features to improve it. According to the proposed changes, a new ontology that covers data used in VirGIS is presented.

Abstract Logic is the foundational discipline of many sciences. Part mathematics, part philosophy and part computing science, logic remains a core intellectual study and is increasingly relevant to practical concerns. It spreads into planning, into program synthesis, into circuit design and into discourse analysis. It underpins the entire science of artiŻcial intelligence. In order to increase knowledge from the field of logic, I participated in the Logic Summer School. This report covers some information.

World Wide Web obsahuje data, která jsou pro počítačové programy nesrozumitelná. Následkem toho je na něm obtížné některé věci zautomatizovat. Nedostatky současného webu by měl odstranit sémantický web, ve kterém data budou mít přesně popsaný význam. Zlepšení může přinést také v oblasti integrace, která je v případě dat pocházejících z webu velmi obtížná. Tento článek se zabývá integrací webových dat. Zaměřuje se na relační data ve formátu XML a navrhuje postupy základních integračních operací.

Knowledge representation in geographic information systems (GIS) and associated data processing presents many challenges for researchers. To use ontologies as knowledge representation belongs to the most topical problems to solve. This involves ontology development as well as ontology re-usage. The goal of the research described in this paper is to develop a specific ontology for a given GIS area.

Ontologies play an important role in knowledge representation. Among various fields, where ontologies can be useful, is the GIS data area. We consider data in a specific GIS domain and develop a new ontology. The result is described in this paper.

A crucial point in automated data processing is the way in which the data are expressed. One possibility is to employ existing features of the Semantic Web - ontologies. Ontologies play an important role in a knowledge representation. The aim of the research presented in this paper is to provide more automated VirGIS system. VirGIS is an integration system that works with GIS (Geographical Information Systems) data. As a first step of our research, we describe its data using common Semantic Web techniques and build a VirGIS ontology.

Integration has been an acknowledged data processing problem for a long time. However, there is no universal tool for general data integration. Because various data descriptions, data heterogeneity, and machine unreadability, it is not easy way. Improvement in this situation could bring the Semantic Web. Its idea is based on machine understandable web data, which bring us an opportunity of better automated processing. The SemanticWeb is still a future vision, but there are already some features we can use. The paper describes how is integration solved in mediation integration system VirGIS and discusses use of nowadays Semantic Web features to improve it. According to the proposed changes, a new ontology that covers data used in VirGIS is presented.

Ontologies play an important role in a knowledge representation. It involves ontology development as well as ontology re-use. Among various fields, where ontologies can be useful, is the GIS (Geographical Information System) data area. The goal of the research described in this paper is to develop a specific ontology for a given GIS domain. At first, we describe a general methodology and main tools for ontology development. Then a new ontology that covers data used in a VirGIS integration system is presented. The paper describes the VirGIS specified ontology as well as a list of spatio-temporal data ontologies that are available and possible to use for a general data features description.

Integration has been an acknowledged problem for a long time. With the aim at combining data from different sources, data integration usually provides a unified global view over these data. A crucial part of the task is the establishment of the connection between the global view and the local sources. Two basic approaches have been proposed for this purpose: Global As View (GAV) and Local As View (LAV).With the Semantic Web and its data description means, there is also another possibility - to employ ontologies for the relationship description in an integration system.

Information Retrieval (IR) as a process of searching relevant information is a significant discipline of a data processing field. European Summer School in Information Retrieval ESSIR provides students, academic and industrial researchers and developers a grounding in the core objects of IR (models, architectures, algorithms), as well as covering some current topics, e.g. information retrieval from the Web. We have participated its 5th year that was held at Dublin City University in Dublin, Ireland.

Datová integrace je uznávaný problém v oblasti zpracování dat. Jejím cílem je obvykle poskytnout ucelený pohled na několik datových zdrojů. V případě nematerializovaného řešení je klíčové stanovení vazeb mezi poskytovaným virtuálním pohledem a daty uloženými ve zdrojích. článek se zabývá řešením stanovení těchto vazeb. Svůj přístup zakládá na ontologiích.

A key point in modern automated data processing is metadata semantics representation. Employing Semantic Web existing features - ontologies - is a promising option. Ontologies open a novel approach to knowledge representation. The paper presents a GIS (Geographic Information System) domain application illustrating ontological approach to data integration and data processing automation in the specific system. This VirGIS system is an integration system that works with spatio-temporal data. We start our study with developing the data representation based on common Semantic Web techniques and build a VirGIS ontology.

Data integration usually provides a unified global view over several data sources. A crucial part of the task is the establishment of the connection between the global view and the local sources. For this purpose, two basic mapping approaches have been proposed: GAV (Global As View) and LAV (Local As View). On the Semantic Web, there can be considered also an ontological approach. In this paper, data integration is solved using ontologies of the sources. To express relationships between the global view and local source schemas, an ontology for the integration system is built. Thus, a schema integration task is transformed to an ontology merging task.

V tomto příspěvku je popsán přístup k virtuální integraci dat využívající současných principů, metod a nástrojů sémantického webu. Přístup pracuje s daty ve formátu RDF a předpokládá dostupnost ontologií, které je popisují. Ontologie jsou základem pro všechny kroky prezentovaného integračního procesu. Jsou využity jak k určení vztahů mezi daty a poskytovaným integrovaným pohledem, tak i k zápisu nalezených korespondencí. Ty jsou dále použity při zpracování dotazů kladených na integrovaná data.

The paper deals with one step of non-materialized data integration - schema matching task. It works with data sources on the Semantic Web; the crucial assumption for the considered task is available ontologies describing data to integrate. Source ontologies are used to find correspondences between source schemas elements. For this, also techniques known from ontology alignment and ontology merging field are used.

Článek se zabývá úlohami, které je třeba řešit při nematerializované integraci dat. Zaměřuje se na hledání korespondencí mezi schématy a mapování schémat. Návrh přístupu řešení těchto úloh na Sémantickém webu těží z dostupných ontologiích popisujících integrované zdroje. Ontologie jsou využity jak k hledání mapování, tak i při jejich popisu.

Článek se zabývá přístupem, jak se pokusit zautomatizovat mnohdy netriviální úlohu nalezení pravidel pro integraci dat. Předkládaný přístup automaticky generuje kandidáty pravidel včetně jejich ohodnocení pomocí nepřímé míry definující jejich prioritu. Priorita může následně být použita buďto návrhářem (člověkem) jako pomocný prvek pro přípravu návrhu, nebo při automatickém návrhu integračního procesu zahrnující pravidla s maximální prioritou. Studie v příspěvku se detailně věnuje dvěma základním typům pravidel, ekvivalenci a hierarchii, přičemž ohodnocení kandidátů je založeno na (strukturální) analýze aktivních domén atributů. V neposlední řadě příspěvek ukazuje možnost decentralizovaného přístupu k integraci dat, jenž je inspirován webovými technologiemi.

In this paper we introduce a new M-tree building method, utilizing the classic idea of forced reinsertions. In case a leaf is about to split, some distant objects are removed from the leaf (reducing the covering radius), and then again inserted into the M-tree in a usual way. A regular leaf split is performed only after a series of unsuccessful reinsertion attempts. We expect the forced reinsertions will result in more compact Mtree hierarchies (i.e., more efficient query processing), while the index construction costs should be kept as low as possible. Considering both low construction costs and low querying costs, we examine several combinations of construction policies with reinsertions. The experiments show that forced reinsertions could significantly decrease the number of distance computations, thus speeding up indexing as well as querying.

In the paper, we propose an approach to an automatic extraction of verification models for the C language source code. We primarily focus on the representation of pointers and arrays, which make the extraction from the C language specific. We provide an implementation of the model extractor as a part of our broader effort to develop a verifier of Windows kernel drivers based on the Zing model checker. To demonstrate the feasibility of our approach, we give examples of the extraction results on a practical synchronization problem.

As XML has undoubtedly become a standard for data representation, it is inevitable to propose and implement techniques for efficient managing of XML data. A natural alternative is to exploit features and functions of (object-)relational database systems, i.e. to rely on their long theoretical and practical history. The main concern of such techniques is the choice of an appropriate XML-to-relational mapping strategy. In this paper we focus on enhancing of user-driven techniques which leave the mapping decisions in hands of users. We propose an algorithm which exploits the user-given annotations more deeply searching the user-specified "hints" in the rest of the schema and applies an adaptive method on the remaining schema fragments. We describe the proposed algorithm, the similarity measure designed for this purpose, sample implementation of key features of the proposal called UserMap, and results of experimental testing on real XML data.

Since XML becomes a crucial format for representing information, it is necessary to establish techniques for managing XML documents. A possible solution can be found in storing XML data in (object-)relational databases. For this purpose most of the existing techniques often exploit an XML schema of the stored XML data, usually expressed in DTD. But the more complex today’s applications are, the more insufficient the DTD becomes and the necessity to use XML Schema language becomes more essential. The paper proposes an algorithm for mapping XML Schema structures to an object-relational database schema (defined by the SQL:1999 standard) using a (modified) DOM interface and an algorithm for storing the valid XML data into relations of the resulting schema. The main aim is to exploit object-oriented features XML Schema has and the advantages of object-relational databases and to preserve the structure as well as semantic constraints of the source schema in the target schema.

Recently XML has achieved the leading role among languages for data representation and thus we can witness a massive boom of corresponding techniques for managing XML data. Most of the processing techniques however suffer from various bottlenecks worsening their time and/or space efficiency.We assume that the main reason is they consider XML collections too globally, involving all their possible features, although real data are often much simpler. Even though some techniques do restrict the input data, the restrictions are often unnatural. In this paper we analyze existing XML data, their structure and real complexity in particular.We have gathered more than 20GB of real XML collections and implemented a robust automatic analyzer. The analysis considers existing papers on similar topics, trying to confirm or confute their observations as well as to bring new findings. It focuses on frequent but often ignored XML items (such as mixed content or recursion) and relationship between schemes and their instances.

As XML has become a standard for data representation, it is inevitable to propose and implement techniques for efficient managing of XML data. A natural alternative is to exploit features and functions of (object-)relational database systems, i.e. to rely on their long theoretical and practical history. The main concern of such techniques is the choice of an appropriate XML-to-relational mapping strategy. In this paper we focus on enhancing of userdriven techniques which leave the mapping decisions in hands of users. We propose an algorithm which exploits the user-given annotations more deeply searching the user-specified “hints” in the rest of the schema and applies an adaptive method on the remaining schema fragments. We describe the algorithm theoretically, discussing the key ideas of the approach, chosen solutions, their reasons, and consequences. Finally, we overview the open issues related to implementation of the proposed algorithm and its experimental testing on real XML data.

As XML technologies have undoubtedly become a standard for data representation, it is inevitable to provide efficient implementations of W3C recommendations. A possible optimization of particular types of techniques can be found in exploitation of similarity of XML data and/or matching of XML patterns. In this paper we provide an overview and classification of such techniques from various points of view. We briefly describe the best known representatives of particular ideas and we discuss their key advantages and disadvantages. The text should serve as a good starting point for proposing an appropriate similarity-based optimization.

As XML has become a standard for data representation, it can be found in plenty of information technologies. A possible optimization of XML-based approaches can be exploitation of similarity of XML data. In this paper we propose a technique for evaluating similarity of XML schema fragments focusing on two often omitted aspects - structural level of similarity and tuning of parameters of the similarity measure. In the former case we exploit the results of statistical analysis of real-world XML data. In the latter case we show that the tuning problem is a kind of constraints optimization problem and can be solved using corresponding approaches. We have analyzed (dis) advantages of two of them, genetic algorithms and simulated annealing, and in further experiments we show that appropriate tuning produces a more precise similarity measure.

As the XML has become a standard for data representation, it is inevitable to propose and implement techniques for efficient managing of XML data. A natural alternative is to exploit features of (object-)relational database systems, i.e. to rely on their long theoretical and practical history. The main concern of such techniques is the choice of an appropriate XML-to-relational mapping strategy. In this paper we focus on enhancing of user-driven techniques which leave the mapping decisions in hands of users who specify their requirements using schema annotations. We describe our prototype implementation called UserMap which is able to exploit the annotations more deeply searching the user-specified “hints” in the rest of the schema and applies an adaptive method on the remaining schema fragments. Using a sample set of supported fixed mapping methods we discuss problems related to query evaluation for storage strategies generated by the system, in particular correction of the candidate set of annotations and related query translation. And finally, we describe the architecture of the whole system.

As the XML has become a standard for data representation, it is inevitable to propose and implement techniques for efficient managing of XML data. A natural alternative is to exploit features of (object-)relational database systems, i.e. to rely on their long theoretical and practical history. The main concern of such techniques is the choice of an appropriate XML-to-relational mapping strategy. In this paper we focus on enhancing of user-driven techniques which leave the mapping decisions in hands of users who specify their requirements using schema annotations.We describe our prototype implementation called UserMap which is able to exploit the annotations more deeply searching the user-specified “hints” in the rest of the schema and applies an adaptive method on the remaining schema fragments. Using a sample set of supported fixed mapping methods we discuss problems related to query evaluation for storage strategies generated by the system, in particular correction of the candidate set of annotations and related query translation. And finally, we describe the architecture of the whole system.

As the popularity of XML as a format for data representation grows the need for storing XML data in an effective way grows as well. Recent research has provide us with effeective solutions based on storing XML data into relational databases and with new technologies based on storing XML data in the native form. However, design of XML databases has not been studied su±ciently yet. In this paper, we suppose a set of XML schemes that describe XML representation of our data in several types of XML documents. We show that we can not usually store the data directly in this representation because it can contain redundancies. To design an optimal database schema we therefore need to locate these redundancies and eliminate them.We describe two types of redundancies in XML data in this paper and show how to utilize a conceptual schema of the XML schemes to locate such redundancies. We also show how to normalize the XML schemes to eliminate these redundancies.

Recently XML is the standard format used for the exchange of data between information systems and is also frequently applied as a logical database model. If we use XML as a logical database model we need a conceptual model for the description of its semantics. However, XML as a logical database model has some special characteristics which makes existing conceptual models as E-R or UML unsuitable. In this paper, the current approaches to the conceptual modeling of XML data are described in an uniform style. A list of requirements for XML conceptual models is presented and described approaches are compared on the base of the requirements.

Recently XML is the standard format used for the exchange of data between information systems and is also frequently applied as a logical database model. If we use XML as a logical database model we need a conceptual model for the description of its semantics. However, XML as a logical database model has some special characteristics which makes existing conceptual models as E-R or UML unsuitable. In this paper, the current approaches to the conceptual modeling of XML data are described in an uniform style. A list of requirements for XML conceptual models is presented and described approaches are compared on the base of the requirements.

Recently XML is the standard format used for the exchange of data between information systems and is also frequently applied as a logical database model. If we use XML as a logical database model we need a conceptual model for the description of its semantics. In this paper, we describe our work on a new conceptual model for XML called XSEM created as a combination of several approaches applied in the area of conceptual modeling for XML.

In this paper we briefly describe a new conceptual model for XML data called XSEM. The model is a combination of several approaches in the area of conceptual modeling of XML data. The model divides the process of conceptual modeling of XML data to two levels. On the first level, a designer designs an overall non-hierarchical conceptual schema of a domain. On the second level, he or she derives different hierarchical representations of parts of the overall conceptual schema using transformation operators. These hierarchical representations describe how the data is organized in an XML form.

We propose a new conceptual model for XML data called XSEM as a combination of several approaches in the area of the conceptual modeling for XML. The model divides the conceptual modeling process of XML data to two levels. On the first level, a designer designs an overall non-hierarchical conceptual schema of a domain. On the second level, he or she derives different hierarchical representations of parts of the overall conceptual schema using transformation op- erators. These hierarchical representations describe how the data is organized in an XML form.

In this paper we briefly describe a new conceptual model for XML data called XSEM and how to use it for modeling XML interfaces of services in service oriented architecture (SOA). The model is a combination of several approaches in the area of conceptual modeling of XML data. It divides the process of conceptual modeling of XML data to two levels. The first level consists of designing an overall non-hierarchical conceptual schema of the domain. The second level consists of deriving different hierarchical representations of parts of the overall conceptual schema using transformation operators. Each hierarchical representation models an XML schema describing the structure of the data exchanged between a service interface and external services.

With the growing popularity of XML there is a need not only to describe the structure of XML data but also its semantics. For the conceptual modelling of XML we can use existing conceptual models. However, special features of XML require extensions of these models. In this paper, we study conceptual modelling of XML keys. We extend the notion of E-R keys to be suitable for modelling the semantics of XML keys and we show how to express them on the XML logical level.

The paper deals with expressing preferences in the framework of the relational data model. Preferences have usually a form of a partial ordering. Therefore the question arises how to provide the relational data model with such an ordering.

This paper describes an option to express our preferences in the framework of relational databases. Preferences have usually a form of a partial ordering. Therefore the question is how to deliver the semantics of ordering to a database system. The answer is quite straightforward.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. To allow nonmonotonic operations, the preferences are defined between sets of relational instances. The aim is the generalization of the relational algebra that is as minimal as possible, in the sense that the formal fundamentlas of the relational data model are preserved. At the same time, the extended model should be formal enough to provide a sound basis for the investigation of other new preference constructors and operations and for new possible applications.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. The preferences are defined between sets of relational instances, which is a nontrivial generalization of the approach aiming at incorporating ordered attribute domains into the relational data model. The main goals are as follows: an effective representation of information representable by a partial order, an intuitive preference construction and its processing throughout the query execution plan, and a suitable data structure to support it all.

The notion of preference poses a new prospect of personalization of database queries. In addition, it can be exploited to optimize query execution. Indeed, a novel optimization technique involving preference is developed, and its algorithm presented.

The paper resumes recent advances in the field of logic of preference and presents their application in the field of database queries. Namely, non-monotonic reasoning mechanisms including various kinds of preferences are reviewed, and a way of suiting them to practical database applications is shown: reasoning including sixteen strict and non-strict kinds of preferences, inclusive of ceteris paribus preferences, is feasible. However, to make the mechanisms useful for practical applications, the assumption of preference specification consistency has to be relinquished. This is achieved in two steps: firstly, all the kinds of preferences are de- fined so that some uncertainty is inherent, and secondly, not a notion of a total pre-order but a partial pre-order is used in the semantics, which enables to indicate some kind of conflict among preferences. Most importantly, the semantics of a set of preferences is related to that of a disjunctive logic program.

The paper resumes a logical framework for formulating preferences and proposes their embedding into relational algebra through a single preference operator parameterized by a set of user preferences of sixteen various kinds, inclusive of ceteris paribus preferences, and returning only the most preferred subsets of its argument relation. Most importantly, conflicting set of preferences is permitted and preferences between sets of elements can be expressed. Formal foundation for algebraic optimization, applying heuristics like push preference, also is provided: abstract properties of the preference operator and a variety of algebraic laws describing its interaction with other relational algebra operators are presented.

The paper gives an overview of recent advances in the field of logic of preference and discusses their applicability in the frame of the relational data model. Namely, non-monotonic reasoning mechanisms with various kinds of preferences are reviewed in detail, and a way of suiting them to practical database applications is presented. These mechanisms enable to reason simultaneously about sixteen strict and non-strict kinds of preferences, including ceteris paribus preferences. To make the mechanisms useful for practical applications, the assumption of preference specification consistency has to be loosened. This is achieved in two steps: firstly, all the preference specifications are generalized to permit uncertainty, and secondly, not a total pre-order on worlds but a partial pre-order on worlds is used in the semantics, which enables to indicate some kind of conflict among worlds by their incomparability. Most importantly, the semantics of set of preferences is related to that of a disjunctive logic program.

Multi-agent systems typically utilize a non-blocking asynchronous communication in order to achieve required flexibility and adaptability. High performance computing techniques exploit the current hardware ability of overlapping asynchronous communication with computation to load the available computer resources efficiently. On the contrary, widely used parallel processes modeling methodologies do not often allow for an asynchronous communication description. At the same time those models do not allow their user to select the granularity level and provide only a fixed set of machine and algorithm description quantities. In this work we addressed this issue and designed a new parallel processes modeling methodology. Its main features include an open set of atomic operations that are calculated and predicted for the algorithm in question, and the computer aided semi-automatic measuring of operation counts and approximation of cost functions. This allows not only for tuning the model granularity as well as accuracy according to user needs, but also to reach a such description complexity that would be very difficult to obtain without any computer aid.

A multi-agent system targeted toward the area of computational intelligence modeling is presented. The purpose of the system is to allow both experiments and high-performance distributed computations employing hybrid computational models. The focus of the system is the interchangeability of computational components, their autonomous behavior, and emergence of new models.

In this paper we present an architecture for decision making of software agents that allows the agent to behave autonomously. Our target area is computational agents — encapsulating various neural networks, genetic algorithms, and similar methods — that are expected to solve problems of different nature within an environment of a hybrid computational multi-agent system. The architecture is based on the vertically-layered and belief-desire-intention architectures. Several experiments with computational agents were conducted to demonstrate the benefits of the architecture.

During the implementation of an environmental accounting system in a company, one of the most important pieces of information to obtain is a detailed understanding about material flows (raw materials, semi-finished products, final products and wastes) and flows of different types of energy inputs (buying, selling and wastage) when thinking about the consequences on the company. The authors, in the article: 1) study the question of the quantification of the flows, and the accuracy of their measurement, 2) provide an environmental accounting statement, with help of standards and indices, statistical trends analysis.

We study and design multi-agent systems for computational intelligence modeling. Agents typically reside in a high-performance parallel environment, such as a cluster of computational nodes, and utilize a non-blocking asynchronous communication. The need of accurate predictions of run-time and other characterizations of complex parallel asynchronous processes bring us to design a new parallel model creation methodology. In this article our approach is briefly described and a test case is shown and discussed.

In this paper, a multi agent platform targeted toward the area of computational intelligence modeling is presented. We show the design of various computational agents creating multi agent systems, as well as the infrastructure capabilities. The focus of the system is the cooperation of agents, their interchangeability autonomous behavior, and emergence of new models. Two main areas of cooperation are presented: automated creation of a multi-agent system satisfying given constraints, and decision support for agent partner selection. It is demonstrated that such a system is able to assist in building hybrid artificial intelligence models based on data in a distributed environment.

In this paper we present an approach where a hybrid computational model is represented as a set of communicating agents composing a multi-agent system. A general concept of representation of connected groups of agents is introduced and utilized for automatic building of schemes to solve a given computational task. We propose a combination of an evolutionary algorithm and a formal logic resolution system which is able to generate and verify new schemes. Furthermore, the adaptive cooperation support of individual computational agents is described, which improves their efficiency in time. These features are implemented within a software system and demonstrated on several examples.

This paper describes ontological description of computational agents, their properties and abilities. The goal of the work is to allow for autonomous behavior and semi-automatic composition of agents within a multiagent system. The system has to be create foundation for the interchangeability of computational components, and emergence of new models. This paper focuses on ways of representing agents and systems in standard formalisms, such as description logics, OWL, and Jade.

A formalism for the logical description of computational agents and multi-agent systems is given. It is explained how it such a formal description can be used to configure and reason about multi-agent systems realizing computational intelligence models. A usage within a real software system Bang 3 is demonstrated. A way to extend the system toward dynamic environments with migrating agents is discussed.

This work deals with a problem of automatic composition of multi-agent system satisfying given constraints. A general concept of representation of connected groups of agents (schemes) within a multi-agent system is introduced and utilized for automatic building of schemes to solve a given computational intelligence task. We propose a combination of an evolutionary algorithm and a formal logic resolution system which is able to propose and verify new schemes. The approach is illustrated on simple examples.

Ontologies are commonly considered as one of the essential parts of the Semantic Web vision, providing a theoretical basis and implementation framework for conceptual integration and information sharing among various domains. In this paper, we present the main principles of a new ontology acquisition framework applied for semi-automatic generation of scientific portals. Extracted ontological relations play a crucial role in the structuring of the information at the portal pages, automatic classification of the presented documents as well as for personalisation at the presentation level.

Recently, the significance of uncertain information representation has become obvious in the Semantic Web community. This paper presents an ongoing research of uncertainty handling in automatically created ontologies. Proposal of a specific framework is provided. The research is related to OLE (Ontology LEarning), a project aimed at bottom-up generation a nd merging of domain specific ontologies. Formal systems that underlie the uncertai nty representation are briefly introduced. We will discuss a universal internal form at of uncertain conceptual structures in OLE then. The proposed format serves as a basis for inference tasks performed among an ontology. These topics are outlined as motivations of our future work.

This paper presents BOLE — a new platform for bottomup generation and merging of bio-ontologies. In contrast to other ontology-learning systems that are currently available, BOLE can be characterized by the modular architecture enabling integrating and comparing various methods of the automatic acquisition of semantic relations. We introduce the architecture of the tool and discuss the methodology of the employed synthetic bottom-up approach. OLITE — the central component responsible for the automatic acquisition of semantic relations from texts is described in detail. The presented preliminary results prove the efficiency of the implemented framework. We also provide a brief comparative overview of other relevant approaches and outline the future work on representation of uncertain knowledge for bio-ontology merging.

This paper presents OLE — a new platform for bottom-up generation and merging of ontologies. In contrast to other ontology-learning systems that are currently available, OLE can be characterized by the modular architecture enabling integrating and comparing various methods of the automatic acquisition of semantic relations. We introduce the architecture of the tool and discuss the methodology of the employed synthetic bottom-up approach. OLITE — the central component responsible for the automatic acquisition of semantic relations from texts is described in detail. The presented preliminary results prove the efficiency of the implemented framework. We also provide a brief comparative overview of other relevant approaches and outline the future work on representation of uncertain knowledge for ontology merging.

The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents our research on uncertainty handling in automatically created ontologies. A new framework for uncertain information processing is proposed. The research is related to OLE (Ontology LEarning) - a project aimed at bottom-up generation and merging of domain-specific ontologies. Formal systems that underlie the uncertainty representation are briefly introduced. We discuss the universal internal format of uncertain conceptual structures in OLE then and offer a utilisation example then. The proposed format serves as a basis for empirical improvement of initial knowledge acquisition methods as well as for general explicit inference tasks.

Current Semantic Web implementation efforts pose a number of challenges. One of the big ones among them is development and evolution of specific resources—the ontologies—as a base for representation of the meaning of the web. This paper deals with the automatic acquisition of semantic relations from the text of scientific publications (journal articles, conference papers, project descriptions, etc.). We also describe the process of building of corresponding ontological resources and their application for semi–automatic generation of scientific portals. Extracted relations and ontologies are crucial for the structuring of the information at the portal pages, automatic classification of the presented documents as well as for personalisation at the presentation level. Besides a general description of the portal generating system, we give also a detailed overview of extraction of semantic relations in the form of a domain–specific ontology. The overview consists of presentation of an architecture of the ontology extraction system, description of methods used for mining of semantic relations and analysis of selected results and examples.

Ontology learning is one of the essential topics in the scope of an important area of current computer science and artificial intelligence - the upcoming Semantic Web. As the Semantic Web idea comprises semantically annotated descendant of the current world wide web and related tools and resources, the need of vast and reliable knowledge repositories is obvious. Ontologies present well defined, straightforward and standardised form of these repositories. There are many possible utilisations of ontologies - from automatic annotation of web resources to domain representation and reasoning tasks. However, the ontology creation process is very expensive, time-consuming and unobjective when performed manually. So a framework for automatic acquisition of ontologies would be very advantageous. In this work we present such a framework called OLE (an acronym for Ontology LEarning) and current results of its application. The main relevant topics, state of the art methods and techniques related to ontology acquisition are discussed as a part of theoretical background for the presentation of the OLE framework and respective results. Moreover, we describe also preliminary results of progressive research in the area of uncertain fuzzy ontology representation that will provide us with natural and reasonable instruments for dealing with inconsistencies in empiric data as well as for reasoning. Main future milestones of the ongoing research are debated as well.

The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents new results of our research on uncertainty handling in ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology LEarning) a project aimed at bottom-up generation and merging of domain-specific ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called ANUIC. We discuss current achievements in taxonomy acquisition and outline some interesting applications of the framework regarding non-traditional reasoning perspectives.

The need of search mechanisms based on data content rather then attributes values has recently lead to formation of the metric-based similarity retrieval. The computational complexity of such retrieval and the large volume of processed data call for distributed processing. In this paper, we propose chiDistance, a distributed data structure for similarity search in metric spaces. The structure is based on the idea of a vectorbased index method iDistance which enables to transform the issue of similarity search into the one-dimensional range search problem. A Peerto-Peer system based on the Chord protocol is created to distribute the storage space and to parallelize the execution of similarity queries. In the experiments conducted on our prototype implementation we study the system performance concentrating on several aspects of parallelism of the range search algorithm.

The need for a retrieval based not on the attribute values but on the very data content has recently led to rise of the metric-based similarity search. The computational complexity of such a retrieval and large volumes of processed data call for distributed processing which allows to achieve scalability. In this paper, we propose M-Chord, a distributed data structure for metric-based similarity search. The structure takes advantage of the idea of a vector index method iDistance in order to transform the issue of similarity searching into the problem of interval search in one dimension. The proposed peer-to-peer organization, based on the Chord protocol, distributes the storage space and parallelizes the execution of similarity queries. Promising features of the structure are validated by experiments on the prototype implementation and two real-life datasets.

Basics of the DataPile structure for data handling systems have been theoretically designed and published. During implementation of such system, numerous problems which were not addressed during the theoretical design phase arose. In a real production environment, the applications connected to the DataPile core need special treatment and set important requirements on the data synchronization process. This article concerns with generating of differential data being distributed from the central DataPile storage to individual applications. It is shown that the synchronization part of DataPile-structured system can be implemented and run efficiently despite of the restrictions or limitations these individual applications impose.

This paper describes the exhaustive results obtained within IST 290388 Project Balkanet, which went on 2001-2004. The attention is paid to the restructuring and final shaping the individual Balkan WordNets. In comparison with the EuroWordNet Project some new results have been obtained: The sets of Base Concepts have been extended and a set of the Balkanet 1. Common Synsets has been introduced (8,000 synsets). These were relinked to Princeton WordNet 2.0 (PWN) and converted to XML standard format, 2. The language specific synsets that do not have translation equivalents in PWN 2.0 have been established for Balkanet languages, 3. Valency frames have been developed for Czech, Bulgarian and Romanian, 4. Domains have been added to Balkanet WordNets and implemented in the VisDic browser, 5. Integrating derivational relations into Czech WordNet and adding semantic relations into Turkish WordNet exploiting Turkish derivational morphology, 6. Links to the SUMO/MILO Ontology were added to and implemented in VisDic.

We design a formal model of an amorphous computer suitable for theoretical investigation of its computational properties. The model consists of a finite set of nodes created by RAMs with restricted memory, which are dispersed uniformly in a given area. Within a limited radius the nodes can communicate with their neighbors via a single-channel radio. The assumptions on low-level communication abilities are among the weakest possible: the nodes work asynchronously, there is no broadcasting collision detection mechanism and no network addresses. For the underlying network we design a randomized communication protocol and analyze its efficiency. The subsequent experiments and combinatorial analysis of random networks show that the expectations under which our protocol was designed are met by the vast majority of the instances of our amorphous computer model.

We design a formal model of an amorphous computer suit- able for theoretical investigation of its computational properties. The model consists of a ¯nite set of nodes created by RAMs with restricted memory, which are dispersed uniformly in a given area. Within a limited radius the nodes can communicate with their neighbors via a single- channel radio. The assumptions on low-level communication abilities are among the weakest possible: the nodes work asynchronously, there is no broadcasting collision detection mechanism and no network addresses. For the underlying network we design a randomized communication pro- tocol and analyze its e±ciency. The subsequent experiments and combi- natorial analysis of random networks show that the expectations under which our protocol was designed are met by the vast majority of the instances of our amorphous computer model.

Amorphous computing differs from the classical ideas about computations almost in every aspect. The architecture of amorphous computers is random, since they consist of a plethora of identical computational units spread randomly over a given area. Within a limited radius the units can communicate wirelessly with their neighbors via a single-channel radio.We consider a model whose assumptions on the underlying computing and communication abilities are among the weakest possible: all computational units are finite state probabilistic automata working asynchronously, there is no broadcasting collision detection mechanism and no network addresses. We show that under reasonable probabilistic assumptions such amorphous computing systems can possess universal computing power with a high probability. The underlying theory makes use of properties of random graphs and that of probabilistic analysis of algorithms. To the best of our knowledge this is the first result showing the universality of such computing systems.

Digitální knihovny (DK) přispívají k rozvoji Sémantického webu a současně mohou využívat jeho technologické prvky. Lze tak docílit kvalitnějšího řízení dat v DK a snazší integrace více DK, jakož i a zvýšení možnosti interakce s dalšími informačními zdroji. Ideou, stojící za Sémantickým webem, je rozšířit webové stránky značkováním, které podchytí alespoň část významu obsahu stránky. Toto sémantické značkování znamená přidání jistých metadat, která poskytují formální sémantiku obsahu webu. Projekty Sémantického webu vycházejí z technologií, které jsou vyvíjeny jako standardy. Patří sem jazyky XML, XML Schema, RDF a RDF Schema. Tyto jazyky slouží pro zápis metadat, z nichž část se organizuje v ontologiích. Další úroveň Sémantického webu využívá jazyky logiky. Základ zpracování v takto pojatém webu poskytují programy - softwaroví agenti. Cílem článku je uvést do technologií Sémantického webu a ukázat jejich uplatnění při vytváření DK

Současné webové vyhledavače založené na technikách vyhledávání informací v textech nejsou schopny využít sémantické znalosti uvnitř webové stránky a tedy nemohou dát uspokojující odpovědi na uživatelské dotazy. Možným řešením se zdá být tzv. Sémantický web, který koncem 90. let popsal ve své vizi Tim Berners-Lee. Ideou, stojící za Sémantickým webem, je rozšířit webové stránky značkováním, které podchytí alespoň část významu obsahu stránky. Toto sémantické značkování znamená přidání jistých metadat, která poskytují formální sémantiku obsahu webu. Projekty Sémantického webu vycházejí z několika technologií, z nichž ty základní jsou již standardizovány nebo alespoň doporučovány. Patří sem jazyky XML, XML Schema, RDF a RDF Schema. Tyto jazyky slouží pro zápis metadat, z nichž některá se organizují v tzv. ontologiích. Další úroveň Sémantického webu využívá jazyky logiky. Základ zpracování v takto pojatém webu poskytují softwaroví agenti, tj. programy, které pracují autonomně a proaktivně. Cílem článku je uvést do technologií podporujících vytváření Sémantického webu, ukázat jeho architekturu a zmínit některé již rozpracované projekty směřující k vytváření inteligentních webových informačních služeb, personalizovaných webových míst a sémanticky zesílených vyhledávacích strojů.

Methods of web data mining can be divided into several categories according to a kind of mined information and goals that particular categories set: Web structure mining (WSM), Web usage mining (WUM), and Web Content Mining (WCM). The objective of this paper is to propose a new WCM method of a page relevance ranking based on the page content exploration. The method, we call it Page Content Rank (PCR) in the paper, combines a number of heuristics that seem to be important for analysing the content of Web pages. The page importance is determined on the base of the importance of terms which the page contains. The importance of a term is specified with respect to a given query q and it is based on its statistical and linguistic features. As a source set of pages for mining we use a set of pages responded by a search engine to the query q. PCR uses a neural network as its inner classification structure. We describe an implementation of the proposed method and a comparison of its results with the other existing classification system – PageRank algorithm.

Ever increasing environmental demands from customers, authorities and governmental organizations as well as new business control functions are integrated to environmental management systems (EMSs). With a production of huge data sets and their processing in real-time applications, the needs for environmental data management have grown significantly. Current trends in database development and an associated research meet these challenges. The paper discusses recent advances in database technologies and attempts to highlight them with respect to requirements of EMSs.

This paper deals with a newly designed and developed system Manatee that can be employed to manage corpora, especially extremely large ones with billions of words, and enables the efficient evaluation of complex queries and the computation of advanced statistics. The main functions of the tool are presented here, together with the introduction of its web-based graphical user interface, Bonito. The sophisticated statistical processing is demonstrated in an example of computing of Word Sketches. Special attention is paid to the definition of the word sketches for Czech and problems connected to its free word order.

Zatímco dílčí ontologie pokrývají jeden pohled na úzce vymezenou oblast, mnohé aplikace vyžadují obecnější přístup k popisovaným datům. Z tohoto důvodu se přistupuje ke srovnávání ontologií (Ontology Matching), které, pokud je to možné, transformuje několik různých ontologických popisů do jediného. Příspěvek popisuje případovou studii takového procesu za využití různých metod, srovnává jejich úspěšnost a diskutuje možnost využití dílčích výsledků k definici výsledné ontologie. Pro experiment byly nezávisle vytvořeny dvě triviální ontologie, které byly různými nástroji a metodami integrovány do jedné.

The paper describes a method for relational data model estimation from input web data and usage of this method. It includes also its principal limitations and shows the model usage for a more effective storage into a repository. The repository is implemented as the universal relation. The properties of the model are described as well.

Příspěvek popisuje provedenou studii proveditelnosti databázově orientované části systému zajišťujícím automatickou extrakci dat z webových zdrojů (formáty XHTML, XML, CSV). Úkolem této části je transformace dat do automaticky vygenerovaného relačního modelu, který může být následně užit pro realizaci myšlenek sémantického webu. V úvodní části je uvedena motivace pro implementaci takového nástroje. Součástí příspěvku je i částečné ohlédnutí za již implementovanými metodami, které autor v současné době zpracovává. V poslední části je nastíněna fuzzyfikace problematiky.

Příspěvek popisuje provedenou studii proveditelnosti databázově orientované části systému zajišťujícím automatickou extrakci dat z webových zdrojů (formáty XHTML, XML, CSV). Úkolem této části je transformace dat do automaticky vygenerovaného relačního modelu, který může být následně užit pro realizaci myšlenek sémantického webu. V úvodní části je uvedena motivace pro implementaci takového nástroje. Součástí příspěvku je i částečné ohlédnutí za již implementovanými metodami, které autor v současné době zpracovává. V poslední části je nastíněna fuzzyfikace problematiky.

The paper proposes a data structure modelling method, which aim is to estimate a structure model from a given input data set. The model can be seen as an estimate of data semantics ֠the obtained relations can be transformed into an RDF or OWL semantic web format documents to be included into the semantic web portfolio. The proposed method makes a connection between current web sources and the semantic web vision to be realized. Finally, the method usage and conversion rules are illustrated on an example.

Metoda odhadování struktury dat spojuje vizi sémantického webu a dnešní webové datové zdroje, které převážně neobsahují žádnou doprovodnou sémantiku prezentovaných informací. Aby bylo možné tyto zdroje použít pokročilými nástroji sémantického webu, je potřeba sémantiku prezentovaných dat alespoň odhadnout. Příspěvek popisuje takovou metodu, ukazuje její použití pro úlohy induktivního logického programování a jmenuje výhody použití pravidlových systémů pro její implementaci.

Odhadování struktury dat je jednou z možností, jak automatizovaným způsobem interpretovat data. Ta mohou být popsána pomocí modelu funkčních závislostí, vytváření takového modelu lze srovnat s některými technikami strojového učení. Tento příspěvek shrnuje vybrané základní techniky induktivního logického programování a analyzuje je z pohledu metody odhadování struktury dat. Ukazuje se, že techniky induktivního logického programování lze v některých případech převést právě odhadování struktury dat.

Odhad struktury dat získaných například z webových zdrojů lze využít jednak pro uložení dat, tak pro netriviální dotazování nad těmito daty. Článek rozšiřuje metodu odhadu struktury dat získávající odpovídající schéma relačního modelu ze vstupních dat a popisuje metodu uložení dat pomocí jednoduchého asociativního úložiště dat právě na základě odhadnutého modelu. Článek diskutuje dvě možné implementace úložiště: první uchovávající data jako instance funkčních závislostí, druhou uchovávající pouze instance funkčních závislosti mezi jednoduchými atributy rozšířenou o podporu komplexních atributů pomocí metainformace.

Použití asociativního úložiště dat je jednou z možností, jak efektivním způsobem reprezentovat data. Článek se zabývá z převážné části metodou učení takového úložiště, přičemž využívá myšlenek vize sémantického webu. Dále ukazuje souvislosti této metody s teoriemi organizace paměti živých organismů včetně člověka a její učení bez snahy tyto procesy zpětně modelovat. Jelikož se nabízí možnost využít současných webových stránek jako vstupních dat, je učící algoritmus navržen inkrementálnì a výhody použití takového adaptivního přístupu jsou detailně popsány. Výsledkem algoritmu je asociativní úložiště navržené na základě všech dostupných (meta)informací, na které je možné pohlížet jako na extensionální úrovni odhadnutou sémantiku dat.

Mechanisms for accessing and training the data repository using a binary matrix formalism are presented. The repository is designed for a data storage through corresponding instances of simple attribute functional dependencies, which can be seen as similar to the binary predicate formalism being used by the RDF semantic web format. Two mechanisms for querying a repository, the generalisation and the specialisation, are given. Furthermore, the incremental repository training mechanism with no extra requirements on the input data form is described: The extensional functional dependency system is used as a generalised view on the stored data; the algorithm is inspired by the functional dependency discovery approach.

The paper compares features of learning and querying process in the situation, when values in the input data set are annotated by attributes or this information is not available. The attribute annotation enables to consider global relationships, which are useful to express the data semantics in a explicit way. It will be shown data can be accessed with no semantic interpretation and then, after the evaluation process, the result can be interpreted.

Machine learning methods can be seen as an optimalisation task reducing differences between an expected and returned result on a given data set. A corresponding knowledge base can be expressed in many ways, for example, by a binary predicate formalism. The talk deals with a minimalisation of predicate ammount in such a repository, which is enabled by a transitivity. The transitive reduction algorithm will be detaily given for an incremental (attribute annotated data driven) building of a knowledge base; a base model with higher expressiveness will be prefered. Finally, an effect of the selected model to estimated explicit semantic definitions of symbols (internal base interpretation) will be mentioned as well.

Vize sémantického webu byla představena před skoro již 10 lety, avšak žádná z její aplikací prozatím nedokázala oslovit takové množství lidí, jaké dnes používá web v současné podobě. Příspěvek se věnuje možnostem sémantického webu a přínosům, které může přinést pro koncové uživatele. Nejprve podává přehled o současných technologiích i jejich použití a následně diskutuje možnosti plynoucí z použití odkazů v prostředí sémantického webu tak, jak je známe z webu současného, tedy rozšiřující, zpřesňující či udávající kontext prezentované informace.

Přıspěvek se zabývá aspekty optimalizace paměťových nároků binárního úložiště atributově anotovaných dat na základě transitivní redukce zobecněného systému funkčních závislostí. Tento systém buď může být předem daný modelem, v tomto případě se ukazuje, že je možné optimalizaci použít jednorázově; a nebo tento model je inkremetálním způsobem odhadován a pak se ukazuje vhodným pouze již jednou naoptimalizované úložiště pouze upravovat opět inkrementálním způsobem. V poslední sekci se příspěvek zaobírá rozborem nejednoznačnosti výsledku včetně detailního rozboru vlastností základních konfigurací částí modelu způsobující tuto nejednoznačnost. V neposlední řadě je analyzována složitost dílčích operací v úložišti.

Cílem příspěvku je předložit vizi nových přístupů pro sdílení a vyhledávání dat na internetu. Opírá se o prověřené technologie pracující nad textovými webovými dokumenty a propojuje je se sémantickým webem, moderním prostředkem pro výměnu dat a aktuálními trendy ve vývoji internetu jako celku.

The vision of the semantic web as a distributed and trusted environment for data sharing together with related issues are presented. The paper brings a basic binary matrix formalism for the internal representation of sources and shows the clasical issues as a data inconsistency and a data integration. Aspects of these issues lead to the binary formalism to be generalised into the <0,1> interval one to enable the consideration of uncertainty at various level. Finally, the need of a source trust definition is presented and discussed with respect to a semantic web.

Exploiting the concepts of social networking represents a novel approach to the approximate similarity query processing. We present an unstructured and dynamic P2P environment in which a metric social network is built. Social communities of peers giving similar results to specific queries are established and such ties are exploited for answering future queries. Based on the universal law of generalization, a new query forwarding algorithmis introduced and evaluated. The same principle is used to manage query histories of individual peers with the possibility to tune the tradeoff between the extent of the history and the level of the queryanswer approximation. All proposed algorithms are tested on real data and medium-sized P2P networks consisting of tens of computers.

In this paper we tackle the issues of exploiting the concepts of social networking in processing similarity queries in the environment of a P2P network. The processed similarity queries are laying the base on which the relationships among peers are created. Consequently, the communities encompassing similar data emerge in the network. The architecture of the presented metric social network is formally defined using the acquaintance and friendship relations. Two version of the navigation algorithm are presented and thoroughly experimentally evaluated. Finally, learning ability of the metric social network is presented and discussed.

Exploiting the concepts of social networking represents a novel approach to the approximate similarity query processing. We present a metric social network where relations between peers, giving similar results, are established on per-query basis. Based on the universal law of generalization, a new query forwarding algorithm is proposed. The same principle is used to manage query histories of individual peers with the possibility to tune the tradeoff between the extent of the history and the level of the query-answer approximation. All algorithms are tested on real data and real network of computers.

This prototype implements the idea of social networking in metric spaces. The metric social network is a peer-to-peer network in which users can share their data without the need to send them to a centralized node. Searching in this system is based on the notion of similarity which is modelled using metric spaces. The architecture of the metric social network is formally defined by using acquaintance and friendship relations. The implementation builds on top of the MESSIF framework library.

XBW [10] is modular program for lossless compression that enables testing various combinations of algorithms. We obtained best results with XML parser creating dictionary of syllables or words combined with Burrows-Wheeler transform - hence the name XBW. The motivation for creating parser that handles non-valid XML and HTML files, has been system EGOTHOR [5] for full-text searching. On files of size approximately 20MB, formed by hundreds of web pages, we achieved twice the compression ratio of bzip2 while running only twice as long. For smaller files, XBWhas very good results, compared with other programs, especially for languages with rich morphology such as Slovak or German. For any big textual files, our program has good balance of compression and run time. Program XBW enables use of parser and coder with any implemented algorithm for compression.We have implemented Burrows-Wheeler transform which together with MTF and RLE forms block compression, dictionary methods LZC and LZSS, and finally statistical method PPM. Coder offers choice of Huffman and arithmetic coding.

The retrieval of objects from a multimedia database employs a measure which defines a similarity score for every pair of objects. The measure should effectively follow the nature of similarity, hence, it should not be limited by the triangular inequality, regarded as a restriction in similarity modeling. On the other hand, the retrieval should be as efficient (or fast) as possible. The measure is thus often restricted to a metric, because then the search can be handled by metric access methods (MAMs). In this paper we propose a general method of non-metric search by MAMs. We show the triangular inequality can be enforced for any semimetric (reflexive, non-negative and symmetric measure), resulting in a metric that preserves the original similarity orderings (retrieval effectiveness). We propose the TriGen algorithm for turning any blackbox semimetric into (approximated) metric, just by use of distance distribution in a fraction of the database. The algorithm finds such a metric for which the retrieval efficiency is maximized, considering any MAM.

When dealing with image databases, we often need to solve the problem of how to retrieve a desired set of images effectively and efficiently. As a representation of images, there are commonly used some high-dimensional vectors of extracted features, since in such a way the content-based image retrieval is turned into a geometric-search problem. In this article we present a study of feature extraction from raw image data by means of the LSI method (singular-value decomposition, respectively). Simultaneously, we show how such a kind of feature extraction can be used for efficient and effective similarity retrieval using the M-tree index. Because of the application to image retrieval, we also show some interesting effects of LSI, which are not directly obvious in the area of text retrieval (where LSI came from).

The M-tree and its variants have been proved to provide an efficient similarity search in database environments. In order to further improve their performance, in this paper we propose an extension of the M-tree family, which makes use of nearest-neighbor (NN) graphs. Each tree node maintains its own NN-graph, a structure that stores for each node entry a reference (and distance) to its nearest neighbor, considering just entries of the node. The NN-graph can be used to improve filtering of non-relevant subtrees when searching (or inserting new data). The filtering is based on using ”sacrifices” selected entries in the node serving as pivots to all entries being their reverse nearest neighbors (RNNs). We propose several heuristics for sacrifice selection; modified insertion; range and kNN query algorithms. The experiments have shown the M-tree (and variants) enhanced by NN-graphs can perform significantly faster, while keeping the construction cheap.

In multimedia systems we usually need to retrieve database (DB) objects based on their similarity to a query object, while the similarity assessment is provided by a measure which defines a (dis)similarity score for every pair of DB objects. In most existing applications, the similarity measure is required to be a metric, where the triangle inequality is utilized to speed up the search for relevant objects by use of metric access methods (MAMs), for example, the M-tree. A recent research has shown, however, that nonmetric measures are more appropriate for similarity modeling due to their robustness and ease to model a made-to-measure similarity. Unfortunately, due to the lack of triangle inequality, the nonmetric measures cannot be directly utilized by MAMs. From another point of view, some sophisticated similarity measures could be available in a black-box nonanalytic form (e.g., as an algorithm or even a hardware device), where no information about their topological properties is provided, so we have to consider them as nonmetric measures as well. From yet another point of view, the concept of similarity measuring itself is inherently imprecise and we often prefer fast but approximate retrieval over an exact but slower one. To date, the mentioned aspects of similarity retrieval have been solved separately, that is, exact versus approximate search or metric versus nonmetric search. In this article we introduce a similarity retrieval framework which incorporates both of the aspects into a single unified model. Based on the framework, we show that for any dissimilarity measure (either a metric or nonmetric) we are able to change the “amount” of triangle inequality, and so obtain an approximate or full metric which can be used for MAM-based retrieval. Due to the varying “amount” of triangle inequality, the measure is modified in a way suitable for either an exact but slower or an approximate but faster retrieval. Additionally, we introduce the TriGen algorithm aimed at constructing the desired modification of any black-box distance automatically, using just a small fraction of the database.

We study behavioural patterns learned by a robotic agent by means of two different control and adaptive approaches - a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforcement Q-learning algorithm. In both cases, a set of rules controlling the agent is derived from the learned controllers, and these sets are compared. It is shown that both procedures lead to reasonable and compact, albeit rather different, rule sets.

This paper presents OASIS – a new tool that enables (semi)automatic conversion of existing knowledge bases, semantic networks, terminological databases and various other resources to complex ontologies into OWL. The tool is implemented as a client of DEB (Dictionary Editor and Browser) which is able to store, index and efficiently retrieve lexical data. The architecture is based on XML and related W3C standards (XSLT, XML Schema, XPath, DOM). The main feature which brings the efficiency of the transformation is the extension of a standard XSLT processor with the ability to obtain additional data from the server through the mechanism of nested queries. This technique allows formulation of complex constraints needed in the conversion to OWL

It is well known that ontologies will become a key piece, as they allow making the semantics of Semantic Web content explicit. In spite of the big advantages that the Semantic Web promises, there are still several problems to solve. Those concerning ontologies include their availability, development and evolution. In the area of information retrieval, the dimension of document vectors plays an important role. Firstly, with higher index dimensions the indexing structures suffer from the "curse of dimensionality" and their efficiency rapidly decreases. Secondly, we may not use exact words when looking for a document, thus we miss some relevant documents. LSI is a numerical method, which discovers latent semantics in documents by creating concepts from existing terms. In this paper we present a basic method of mapping LSI concepts on given ontology (WordNet), used both for retrieval recall improvement and dimension reduction. We offer experimental results for this method on a subset of TREC collection, consisting of Los Angeles Times articles.

In the area of information retrieval, the dimension of document vectors plays an important role. We may need to find a few words or concepts, which characterize the document based on its contents, to overcome the problem of the "curse of dimensionality", which makes indexing of highdimensional data problematic. To do so, we earlier proposed a Wordnet and Wordnet+LSI (Latent Semantic Indexing) based model for dimension reduction. While LSI works on the whole collection, another procedure of feature extraction (and thus dimension reduction) exists, using binary factorization. The procedure is based on the search of attractors in Hopfield-like associative memory. Separation of true attractors (factors) and spurious ones is based on calculation of their Lyapunov function. Being applied to textual data the procedure conducted well and even more it showed sensitivity to the context in which the words were used. In this paper, we suggest that the binary factorization may benefit from the Wordnet filtration.

In this paper, the web pages concerning products sale are analyzed with the aim to create clusters of similar web pages and characterize these by GUI patterns. We applied GD-CLS (gradient descent - constrained least squares) method which combines some of the best features of other methods. Both traditional methods for searching clusters and nonnegative matrix factorization are used.

This paper describes a prototype of the OCR math engine built in the DML-CZ project. Solution stands on the combination of FineReader and InftyReader programmes. The achieved error rate (counting not only character errors, but also errors in the recognition of structure of mathematics notation) decreased from an initial 12% to under 1%.

Advances in cellular mobile technology have engendered a new paradigm of computing, called mobile computing. New challenges have arisen and solutions are proposed based on various approaches. One of the most important challenges is security and now a day has been found ubiquitous in computing as whole. The paper is intended as a quick survey emphasizing security paradigm and also ad hoc networks are kept in mind and briefly discussed.

In the paper, we present a quality of service and energy aware communication protocol, called RollingBall. We do believe that QoS and energy awareness are two of the most important parameters in wireless sensor networks. The protocol is completely distributed with no centralized control. The key idea is to introduce a resistance calculation for every connection in the network. The resistance reflects the distance to the sink together with energy capabilities of particular sensor. While the resistance is continually re-calculated, packets are sent to the sink via an appropriate path. Such a scheme allows to spend minimum messages on network management, whereby sensor network lifetime is extended and throughput remains high.

The paper describes a possible treatment of sharing data in a large network of users. The mathematical model is based on weighted hypergraphs whose nodes and edges denote the users and their relations, respectively. Its flexibility guarantees to have basic relations between users robust under frequent changes in the network connections. Approach copes with the communication/computing issues from different point of view based on a structure evolution and its further optimization in sense of keeping the parallel space and time complexities low. Although the idea is aimed to the field of mobile computing, it can be generalized in straightforward way to other similar environment. An experimental application is also proposed and discussed in the paper.

The paper describes selected problems and possible solutions for the position management in mobile computing. A proposed scheme extends existing approaches. The main idea is to reduce amount of possible solutions given by a movement prediction algorithm by constrains ubiquitously found in the real-life. Existing solutions and possibilities for a future research are also described.

The thesis deals with security hazards in distributed environments where traditional centralized approaches are only of limited serviceability. One of the very successful model for treating security and access management in distributed systems are so called reputation systems. The main goal of the rep- utation systems is to provide entities in the environment with mechanisms for inferring and building trust consequently used for access control. If the trust between two entities is high enough, transactions are likely to be allowed. The thesis proposes a new security model with trust management system for dynamic and distributed environments with huge number of entities. In dynamic systems new entities or relationships are likely to emerge or existing entities or relationships may often disappear. Such dynamics pose severe problems even for traditional reputation systems. Therefore our approach differs from the traditional ones in the way adopted for establishment and management of trust between entities in our point of view trust is not assigned to particular relationships but the trust is common for a group of entities. In this way, our proposal significantly enhances ability to infer trust between entities with no previous personal experiences with each other or in environments with huge number of entities. For the proposal differs in understanding of trust, it uses a hypergraph model for representation of system of entities. The security model proposed in the thesis contains two algorithms for transformation of a general input graph structure into hypergraph model, an algorithm treating dynamics of the distributed environment and a security subsystem. Our experimental implementation SecGrid utilizes proposed algorithms and it is used for experimental verification of the security models. The experiments investigate ability of the transformation algorithms; in details the dynamic part of our proposal together with the security subsystem proposed specially for the hypergraph model. Experiments show that our model overcame the traditional graph model in many ways especially in dynamic environments with huge amount of entities.

The paper presents a new approach for treating security issues in various environments with special emphasis on Mobile databases, Semantic web and Grids. A brief overview on possible security models and a discussion on their advantages and disadvantages is given. Our model based on virtual organization and is build up on mathematical background based on hypergraphs. We show that hypergraphs are the way how to reduce space complexity of the model. The complexity is important with respect to target environments where number of users might be huge. To verify our model an experimental implementation was programed and some graphical outputs are mentioned.

This paper gives a short overview on security issues widely found in the Semantic Web environment. It goes through each level of the proposed Semantic Web layers and discusses security, privacy and trust for each. Then, a list of possible solutions is given. In particular XML security, RDF security, secure information integration and trust on the Semantic Web are mentioned and short discussion is given. Finally, an approach for treating security and trust based on Virtual organization is described and its advantages are provided.

The paper describes a new approach for treatment security issues in reconfigurable groups of users (Virtual Organizations-VO). The proposed strategy combines a convenient mathematical model, efficient combinatorial algorithms which are robust with respect to changes in the VO structure, and an efficient implementation. The mathematical model uses properties of weighted hypergraphs. Model flexibility enables description of basic security relations between the nodes such that these relations are preserved under frequent changes in connections of the hypergraph nodes. The proposed implementation makes use of the techniques developed for time and space-critical applications in numerical linear algebra. The ideas can be generalized to other concepts describable by weighted hypergraphs. The consistency of the proposed ideas for security management in the changing VO was verified in a couple of tests with our pilot implementation SECGRID.

The technical report presents a brief overview on web search engines with deeper insight into their linear algebra background. The linear algebra plays very important role in modern web search algorithms (e.g. Google). The report presents two algorithms, particularly HITS and PageRank. The algorithms are discussed on their convergence problems and also some improvements to their personalization abilities. The computation complexity is also mentioned and briefly sketched.

The paper describes a new approach for treating trust in reconfigurable groups of users with special accent on trust in the next generations of the Internet. The proposed model uses properties of weighted hypergraphs. Model flexibility enables description of relations between nodes such that these relations are preserved under frequent changes. The ideas can be straightforwardly generalized to other concepts describable by weighted hypergraphs. The consistency of the proposal was verified in a couple of experiments with our pilot implementation SecGRID.

The paper presents the BlueGame project an ad-hoc multiplayer mobile game based on the Dungeons&Dragons board game. The main idea lies in the adoption of Bluetooth Piconet configuration and direct face to face contact of players in real environments.

Despite the tremendous research activity in the field of searching engines for the Internet, current searching engines still face some severe limitations. The paper presents an idea of a distributed data source environment to be build on the current state of the art technologies available on the Internet. The paper combines recent advances in the fields of a data inconsistency, a data integration and reputations of sources for further refinements of data searching and sharing processes. The paper generalizes the data binary formalism narrowly connected with the ideas of the semantic web into the <0,1> interval to enable the consideration of uncertainty at various levels.

At present the eXtensible Markup Language (XML) is used almost in all spheres of human activities. Its popularity results especially from the fact that it is a self-descriptive metaformat that allows to define the structure of XML data using other powerful tools such as DTD or XML Schema. Consequently, we can witness a massive boom of techniques for managing, querying, updating, exchanging, or compressing XML data. On the other hand, for majority of the XML processing techniques we can find various spots which cause worsening of their time or space efficiency. Probably the main reason is that most of them consider XML data too globally, involving all their possible features, though the real data are often much simpler. If they do restrict the input data, the restrictions are often unnatural. In this contribution we discuss the level of complexity of real XML collections and their schemes, which turns out to be surprisingly low. We involve and compare results and findings of existing papers on similar topics as well as our own analysis and we try to ¯nd the reasons for these tendencies and their consequences.

At present the eXtensible Markup Language (XML) is used almost in all spheres of human activities. We can witness a massive boom of techniques for managing, querying, updating, exchanging, or compressing XML data. On the other hand, for majority of the XML processing techniques we can find various spots which cause worsening of their time or space efficiency. Probably the main reason is that most of them consider XML data too globally, involving all their possible features, though the real data are often much simpler. If they do restrict the input data, the restrictions are often unnatural. We discuss the level of complexity of real XML collections and their schemes, which turns out to be surprisingly low. We involve and compare results and findings of existing papers on similar topics as well as our own analysis and we try to ¯nd the reasons for these tendencies and their consequences.

While partial ontologies cover view at one-track area, many applications require much more general approach to describe their data. On this account it approaches to ontology matching as a headstone of further operations, that can transform several ontological descriptions into one. This paper describe case study of such process with using different methods, confront their fruitfulness and discuss a possibility of using particular results to definition of final ontology. Two trivial ontologies were created (independently of any tool) and they were matched using various selected tools.

Internet je ohromným zdrojem provázaných, ale většinou neuspořádaných dat. Sémantický web, jako rozšíření webu současného, se snaží tuto neuspořádanost řešit a to nejen bezprostředně pro lidského uživatele, ale zejména z hlediska možnosti strojového zpracování informací. Cílem je doplnit data o metadata, která mají být srozumitelná jak pro člověka, tak pro počítač. Tato metadata jsou nejčastěji vyjádřena pomocí ontologií, které jsou jedním ze základních stavebních prvků sémantického webu. V příspěvku se snažím nastínit některé z možností integrace (slučování) ontologií za účelem sdílení informací.

In this paper we present an architecture for decision making of software agents that allows the agent to behave autonomously. Our target area is computational agents—encapsulating various neural networks, genetic algorithms, and similar methods — that are expected to solve problems of different nature within an environment of a hybrid computational multi-agent system. The architecture is based on the vertically-layered and beliefdesire-intention architectures. Several experiments with computational agents were conducted to demonstrate the benefits of the architecture

In MANETs and similar link-free networks of communicating objects there is no central authority for naming and connection management. Autoconfiguration of network nodes is therefore desirable and, building on approaches in IPv6, a number of ‘zero configuration’ networking protocols have been proposed for this case. Typically these protocols do not easily scale and have difficulty with network partitioning and merging. In this paper we propose a number of novel, decentralized techniques for name resolution in zero configuration protocols that are more flexible and yet lead to name extensions of smallest possible length, assuming that objects sufficiently mix within their ranges. Finally, the techniques are converted into a fully decentralized, scalable autoconfiguration protocol for use in ad hoc networks with directional antennas. The protocol is ‘lazy’ in the sense that name resolution is postponed until the moment that conflicts prevent the correct functioning of the communication structure.

We present an overview of our results on model theoretic and fixpoint semantics for a relational algebra using a model of many valued Datalog with similarity. Using our previous results on equivalence of our model and certain variant of generalized annotated programs, we base our querying on fuzzy aggregation operators (also called annotation terms, combining functions, utility functions). Using of fuzzy aggregation operators (distinct from database aggregations) enables us to reduce tuning of various linguistic variables. In practice we can learn fuzzy aggregator operators by an ILP procedure for every user profile. Our approach enables also integration of data from different sources via aggregation and similarity. Extending domains we discuss difference between fuzzy elements and fuzzy subsets. We also discuss an alternative, when all extensional data are stored crisp and fuzziness is in rules interpreting data, context and in user query.

In this position paper we share ideas on modeling querying web resources by (imperfect) combination of particular user preferences based on description logic. Our basic assumption is, that web resources are modeled crisp. Imperfection (uncertainty, vagueness,...) comes from user context and preferences. We offer a model based on connection between three EL-description logic systems: classical, annotated(fuzzy) and a new variant of Bayesian description logic. The Bayesian part enables learning each single user`s combination function and concepts.

We present a design of cognitive system architecture with an internal world model. The internal world model is realized with the help of artificial mirror neurons. We consider generalized artificial mirror neurons acting both as a mechanism for assembling and learning multimodal sensorimotor information and as associative memory for invoking multimodal information given only some of its components. We show that within an artificial cognitive system a network of generalized mirror neurons can simultaneously serve as an internal world model recognized by the agent and as that of the agent`s position within this world. We also specify a self-organizing control mechanism, which is based on the basic operations over concepts that were essentially identified by the British 18th century philosopher David Hume. This control mechanism makes use of the internal world model constructed in agent`s interaction with real world and straightforwardly supports imitation learning. Building heavily on the properties of the generalized mirror net and on automatic abstract concept creation, we offer an algorithmic explanation of computational language acquisition, thinking and consciousness in our model. Rather than describing an implementation of the respective mechanisms, the aim of the paper is to establish a proof of the principle of algorithmic nature of higher cognitive functions.

Moderní teorie kognitivních systémů pohlíží na tyto systémy jako na autonomní vtělené výpočetní systémy, které se situují v okolí prostřednictvím svých senzomotorických jednotek. Přesto zejména v kruzích počítačových teoretikù je opakovaně slyšet názory, že na kognici lze pořád možné pohlížet i "klasicky", jako na problém specifického zpracování dat a že tudíž vtělení není nezbytné pro zachycení podstaty kognice. Ukážeme, že takto zjednodušený pohled opomíjí podstatnou vlastnost kognitivních systémů - a sice jejich aktivní vliv na výběr či dokonce vznik vstupních dat. Bez této zpětné vazby si systém nemůže vytvořit svůj vnitřní model světa poznaný prostřednictvím svých akcí. Pro vysvětlení povahy zmíněného problému použijeme výpočetní model kognitivních systémů zavedený autorem v předchozích pracích. Tento model umožní na principielní úrovni přemýšlet o fungování algoritmických mechanizmů imitace, komunikace, vzniku řeči, myšlení a vědomí a tím přispět i k jejich pochopení v živých systémech.

We introduce a computational model of a so-called globular universe which represents generalization of both classical cellular automata and contemporary models of self-assembly. Similarly as the latter mentioned model our model utilizes a multiset of globules which are endowed by self-organizing ability controlled by a finite state mechanism; these computational units are not fixed in a predetermined structure. The environment abounds in these units which are available at places where needed for a self-assembly of various objects. Within a globular universe we define the notion of self-reproducing evolutionary automaton. This notion refers to an automaton being at the beginning of a lineage of self-reproducing automata which leads to self-reproducing automata with arbitrary complex finite state control mechanisms via a series of mutations of intermediate automata. The ideas presented in this paper complement von Neumann`s results on self-reproducing automata in a static universe by offering a precise definition of what is meant by "evolutionary self-reproduction" and by designing a dynamic nondeterministic universe with a self-reproducing self-assembling evolutionary automaton.

We introduce so-called biomata which represent a novel approach to the construction of self-reproducing automata within the automata theory. The design of our automata has been motivated by the ideas of cellular biology on the origin of life. Unlike the von Neumann`s model our model replicates by fission and need not give much attention to the exact guiding of its own assemblage; rather, this process relies on self-assembly abilities of the respective parts produced by the biomaton from input objects not possessing such quality. The model represents an interesting fusion of computational and self-organizational processes.We believe that by capturing the basic aspects of the assumed origin of real life our modelling leads to a conceptually simpler and hence more plausible scenario of natural self-reproduction than the previous attempts did.

Moderní teorie kognitivních systémů pohlíží na tyto systémy jako na autonomní vtělené výpočetní systémy, které se situují v okolí prostřednictvím svých senzomotorických jednotek. Přesto zejména v kruzích počítačových teoretiků je opakovaně slyšet názory, že na kognici lze pořád možné pohlížet i „klasicky“, jako na problém specifického zpracování dat a že tudíž vtělení není nezbytné pro zachycení podstaty kognice. Ukážeme, že takto zjednodušený pohled opomíjí podstatnou vlastnost kognitivních systémů − a sice jejich aktivní vliv na výběr či dokonce vznik vstupních dat. Bez této zpětné vazby si systém nemůže vytvořit svůj vnitřní model světa poznaný prostřednictvím svých akcí. Pro vysvětlení povahy zmíněného problému použijeme výpočetní model kognitivních systémů zavedený autorem v předchozích pracích. Tento model umožní na principielní úrovni přemýšlet o fungování algoritmických mechanizmů imitace, komunikace, vzniku řeči, myšlení a vědomí a tím přispět i k jejich pochopení v živých systémech.

Existujú v súčasnej informatike výzvy zrovnateľné s veľkými problémami, s ktorými je konfrontované ľudstvo v oblasti fyziky, biológie, či iných vedných a inžinierskych oblastí? Má súčasná informatika výskumné zámery svojim významom zrovnateľné s odhalením podstaty gravitácie? S cestou na Mars? S rozlúštením genetického kódu? V prednáške sa zamyslíme, ktoré problémy v informatike majú podobný charakter, prečo je dobré sústrediť sa na ich riešenie a aké riešenia sa rysujú.

We present two original computational models - globular universe and autopoietic automata - capturing the basic aspects of an evolution: a construction of self-reproducing automata by self-assembly and a transfer of algorithmically modified genetic information over generations. Within this framework we show implementation of autopoietic automata in a globular universe. Further, we characterize the computational power of lineages of autopoietic automata via interactive Turing machines and show an unbounded complexity growth of a computational power of automata during the evolution. Finally, we define the problem of sustainable evolution and show its undecidability.

We look for computational limits of artificial, natural and hybrid cognitive and intelligent systems. The common basis for such studies is offered by computationalism, i.e., the belief that cognitive or intelligent processes, respectively, are in essence computational processes. We show that in principle cognitive systems might exist whose computational power outperforms that of Turing machines and that even in practice we observe the rudiments of such systems. These results point to the fact that the so - called Church - Turing Thesis, dealing with the central position of Turing machines in the world of computations and algorithms, must be seen in the context of physical principles exploited by the cognitive systems, and in that of the communication scenario between the system and its environment.

Popíšeme původní výpočetní modely – globulární vesmír a autopoietické automaty – které zachycují podstatné výpočetní aspekty evoluce: konstrukci sebereprodukčních evolučních automatů pomocí sebesestavování a přenos algoritmicky modifikovatelné genetické informace na potomka. V tomto rámci ukážeme neomezený růst výpočetní síly automatů během evoluce a pomocí interaktivního Turingova stroje chrakterizujeme výpočetní sílu rodových linií automatů.

Nastíníme jednoduchou, ale přesto kognitivně účinnou architekturu kognitivního agenta. Náš model se liší od jiných podobných modelů především využíváním dvou komplementárních vnitřních modelů světa, které mají jiný úkol než v podobných modelech známých z odborné literatury. První z nich zachycuje senzorimotorickou „syntaxi“ agentova chování a je využíván pro situování agenta v jeho prostředí. Druhý model popisuje senzorimotorickou dynamiku světa agenta a je využíván pro řízení agentova chování. Informace v obou vnitřních modelech závisí od agentova ztělesnění a jeho zkušeností. Ukážeme, že kognitivní potenciál našeho modelu podstatně překračuje možnosti dřívějších modelů tím, že podporuje algoritmické procesy podobající se ve svých důsledcích vyšším kognitivním funkcím, jakými jsou imitační učení a rozvoj komunikace, řeči, myšlení a vědomí.

Bakteriod je formální abstraktní hybridní systém, který ve své činnosti kombinuje výpočetní a nevýpočetní mechanizmy. Ukážeme, že v prostředí umělých molekul, nadanými jistými samoorganizačními schopnostmi, některé bakteroidy vykazují znaky minimálního života: jsou autonomní, replikují se a mají schopnost darwinovské evoluce. Návrh bakteroidů je inspirován představami současné molekulární biologie o dnes již neexistujících (či zatím neobjevených) formách protoživota.

V příspěvku budeme hledat výpočetní meze kognitivních a inteligentních systémů, a to jak biologických, tak i umělých a hybridních, které jsou kombinacích obou předchozích druhů. Společnou platformu poskytne komputacionalimus, tj. víra, že kognitivní resp. inteligentní procesy jsou v konečném důsledku výpočetními procesy. Ukážeme, že v principu mohou existovat kognitivní systémy, a dokonce i v praxi existují „zárodky“ takových systémů, které předčí svou výpočetní sílou výpočetní sílu Turingových strojů. Tyto výsledky naznačují, že tzv. Church-Turingovu tezi, hovořící o centrálním postavení Turingových strojů ve světě výpočtů a algoritmů, je třeba vidět v souvislosti s fyzikálními principy, které kognitivní systém při své činnosti využívá, a se způsobem, kterým systém komunikuje s okolím.

Amorphous computing differs from the classical ideas about computations almost in every aspect. The architecture of amorphous computers is random, since they consist of a plethora of identical computational units spread randomly over a given area. Within a limited radius the units can communicate wirelessly with their neighbors via a single-channel radio. We consider a model whose assumptions on the underlying computing and communication abilities are among the weakest possible: all computational units are finite state probabilistic automata working asynchronously, there is no broadcasting collision detection mechanism and no network addresses. We show that under reasonable probabilistic assumptions such amorphous computing systems can possess universal computing power with a high probability. The underlying theory makes use of properties of random graphs and that of probabilistic analysis of algorithms. To the best of our knowledge this is the first result showing the universality of such computing systems.

A computational model of molecularly communicating mobile nanomachines is de¯ned. Nanomachines are modelled by timed probabilistic automata augmented by a severely restricted communication mechanism. We show that for molecular communication among such machines an asynchronous stochastic protocol originally designed for wireless communication in so-called amorphous computers with static computational units can also be used. We design an algorithm that using randomness and timing delays selects with a high probability a leader from among sets of anonymous candidates. This enables a simulation of counter automata proving that networks of mobile nanomachines possess universal computing power.

We propose a simple yet cognitively powerful architecture of an embodied conscious agent whose functioning is in a good agreement with neurophysiological evidence. Our model differs from other proposals by exploiting two complementary internal world models having a different purpose than that in similar models known from the literature. The first model captures the sensorimotor "syntax" of the agent's behavior and is used for situating the agent in its environment. The second model describes the sensorimotor dynamics of the world and is used for controlling the agent's behavior. Both internal world models are built automatically, autonomously in the course of agent's interaction with the environment and their coop- eration takes care of symbol grounding. We show that the cognitive potential of the proposed model goes substantially beyond that of earlier models since it supports algorithmic processes underlying phenomena similar to higher cognitive functions such as imitation learning and the development of commu- nication, language, thinking and consciousness.

Nastíníme jednoduchou, ale přesto kognitivně účinnou architekturu inteligentního agenta. Model využívá dvou komplementárních vnitřních modelů světa: jeden pro „syntax“ poznaného světa a druhý pro jeho sémantiku. Tyto modely řeší problém porozumění konceptům a podporují algoritmické procesy, jejichž efekty se pro pozorovatele jeví jako projevy vyšších kognitivních funkcí, jakými jsou imitační učení, rozvoj komunikace, řeči, myšlení a vědomí.

Classical models of computation no longer fully correspond to the current notions of computing in modern systems. Even in the sciences, many natural systems are now viewed as systems that compute. Can one devise models of computation that capture the notion of computing as seen today and that could play the same role as Turing machines did for the classical case? We propose two models inspired from key mechanisms of current systems in both artificial and natural environments: evolving automata and interactive Turing machines with advice. The two models represent relevant adjustments in our apprehension of computing: the shift to potentially non-terminating interactive computations, the shift towards systems whose hardware and/or software can change over time, and the shift to computing systems that evolve in an unpredictable, non-uniform way. The two models are shown to be equivalent and both are provably computationally more powerful than the models covered by the old computing paradigm. The models also motivate the extension of classical complexity theory by non-uniform classes, using the computational resources that are natural to these models. Of course, the additional computational power of the models cannot in general be meaningfully exploited in concrete goal-oriented computations.

With the increasing number of applications that base searching on similarity rather than on exact matching, novel index structures are needed to speedup execution of similarity queries. An important stream of research in this direction uses the metric space as a model of similarity. We explain the principles and survey the most important representatives of index structures. We put most emphasis on distributed similarity search architectures which try to solve the difficult problem of scalability of similarity searching. The actual achievements are demonstrated by practical experiments. Future research directions are outlined in the conclusions.

Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm. Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm.