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.

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.

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.

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

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

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.

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

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

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

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

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

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

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

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

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

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).

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.