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.

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.

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 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.

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.

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 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.