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.

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.

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.

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.

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 .

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.