The metric space paradigm has recently received attention as an important model of similarity in the area of Bioinformatics. Numerous techniques have been proposed to solve similarity (range or nearest-neighbor) queries on collections of data from metric domains. Though important representatives are outlined, this chapter is not trying to substitute existing comprehensive surveys. The main objective is to explain and prove by experiments that similarity searching is typically an expensive process which does not easily scale to very large volumes of data, thus distributed architectures able to exploit parallelism must be employed. After a review of applications using the metric space approach in the field of Bioinformatics, the chapter provides an overview of methods used for creating index structures able to speedup retrieval. In the metric space approach, only pair-wise distances between objects are quantified, so they represent the level of dissimilarity. The key idea of index structures is to partition the data into subsets so that queries are evaluated without examining entire collections -- minimizing both the number of distance computations and the number of I/O accesses. These objectives are obtained by exploiting the property of metric spaces called the triangle inequality which states that if two objects are near a third object, they cannot be too distant to one another. Unfortunately, computational costs are still high and the linear scalability of single-computer implementations prevents from searching in large and ever growing data files efficiently. For these reasons, we describe very recent parallel and distributed similarity search techniques and study performance of their implementations. Specifically, Section 12.1 presents the metric space approach and its applications in the field of Bioinformatics. Section 12.2 describes some of the most popular centralized disk-based metric indexes. Consequently, Section 12.3 concentrates on parallel and distributed access methods which can deal with data collections that for practical purposes can be arbitrary large, which is typical for Bioinformatics workloads. An experimental evaluation of the presented distributed approaches on real-life data sets is presented in 12.4. The chapter concludes in Section 12.5.

Continuous t-norm based fuzzy predicate logic is surveyed as a generalization of classical predicate logic; then a kind of fuzzy description logic based on our fuzzy predicate logic is briefly described as a powerful but still decidable formal system of description logic dealing with vague (imprecise) concepts.

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.

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.

Nastíníme jednoduchou, ale přesto kognitivně účinnou architekturu inteligentního agenta. Model využívá dvou komplementárních vnitřních modelů světa: jeden pro „syntax“ poznaného světa a druhý pro jeho sémantiku. Tyto modely řeší problém porozumění konceptům a podporují algoritmické procesy, jejichž efekty se pro pozorovatele jeví jako projevy vyšších kognitivních funkcí, jakými jsou imitační učení, rozvoj komunikace, řeči, myšlení a vědomí.

With the increasing number of applications that base searching on similarity rather than on exact matching, novel index structures are needed to speedup execution of similarity queries. An important stream of research in this direction uses the metric space as a model of similarity. We explain the principles and survey the most important representatives of index structures. We put most emphasis on distributed similarity search architectures which try to solve the difficult problem of scalability of similarity searching. The actual achievements are demonstrated by practical experiments. Future research directions are outlined in the conclusions.