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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.