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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.