In the currently highly competitive environment, the information systems of the businesses should not only effectively support the existing business processes, but also have to be dynamically adaptable to the changes in the environment. There are increasing efforts at separation of the application and the business logic in the information system. One of the appropriate instruments for this separation is the business rule approach. Business rules are simple, understandable rules. They can be used for the knowledge externalization and sharing also as for the active control and decisions within the business processes. Although the business rule approach is used for almost 20 years, the various specifications and practical applications of business rules are still a goal of the active research. The disadvantage of the business rule approach is great demands on obtaining of the rules. There has to be a domain expert, who is able to manually write them. One of the problems addressed by the current research is the possibility of (semi)automatic acquisition of business rules from the different resources - unstructured documents, historical data etc. This dissertation thesis addresses the problem of acquisition (learning) of business rules from the historical data of the company. The main objective of this thesis is to design and validate a method for (semi)automatic learning of business rules using the data mining of association rules. Association rule are a known data mining method for discovering of interesting relations hidden in the data. Association rules are comprehensible and explainable. The comprehensibility of association rules is suitable for the use of them for learning of business rules. For this purpose the user can use not only simple rules discovered using the algorithm Apriori or FP-Growth, but also more complex association rules discovered using the GUHA method. Within this thesis is used the procedure 4ft-Miner implemented in the data mining system LISp Miner. The first part of this thesis contains the description of the relevant topics from the research of business rules and association rules. Business rules is not a name of one specification of standard but rather a label of the approach to modelling of business logic. As part of the work there is defined a process of selection of the most appropriate specification of business rules for the selected practical use. Consequently, the author proposed three models of involving of data mining of association rules into business rule sets. These models contain also the definition of a model for the transformation of GUHA association rules in the business rules for the system JBoss Drools. For the possibility of learning of business rules using the data mining results from more than one data set, the author proposed a knowledge base. The knowledge base is suitable for the interconnection of business rules and data mining of association rules. From the perspective of business rules the knowledge base is a term dictionary. From the perspective of data mining the knowledge base contains some background knowledge for data preprocessing and preparation of classification models. The proposed models have been validated using practical implementations in the systems EasyMiner (in conjunction with JBoss Drools) and Erian. The thesis contains also a description of two model use cases based on real data from the field of marketing and the field of health insurance.

This theses demonstrate practical use of knowledge discovery in the field of cardiac surgery. The tasks of the Department of Cardiac Surgery University Hospital Olomouc are solved through the use of GUHA method and LISp-Miner system. Mitral valve surgery data comes from clinical practice between the years 2002 and 2011. Theoretical part includes chapter on KDD -- type of tasks, methods and methodology and chapter on cardiac surgery -- anatomy and functions of heart, mitral valve disease and diagnostic methods including quantification. Practical part brings solutions of the tasks and whole process is described in the spirit of CRISP-DM.

Internet roku 2015 se potýká s problémy, které jsou důsledky špatného designu pojmenovávání a adresování v TCP/IP a jež mají přeneseny vliv i na škálovatelnost směrování. Problémy jako růst páteřních směrovacích tabulek, neefektivní multihoming sítí či mobilita zařízení a mnohé další zadávají k otázce, jestli není třeba architekturu Internetu pozměnit. V teoretické části je kvantifikován dopad problémů, možná řešení a zejména je formálně definována teorie kompilujicí poznatky významných publikací zabývajících se problematikou pojmenování, adresování a směrování v počítačových sítí. Tato práce se zabývá dvěma konkrétními technologiemi, jež mají ambicí Internet měnit - Locator/Id Separation Protocol a Recursive InterNetwork Architecture. Výstupem práce jsou vylepšení funkcionality obou výše zmíněných technologií. Za účelem praktického ověření dopadů našeho výzkumu jsou vyvinutý a popsány nové simulační modely pro OMNeT++, které jsou věrné úrovni detailu popisu ze specifikací.

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Cette thèse concerne l'étude de différentes techniques modernes de génie logiciel qui peuvent être mises en ¦uvre pour développer des systèmes de Traitement Automatique des Langues Naturelles de façon générique et extensible. La première partie fait le point sur l'état de l'art en TALN à propos des Langages Spécialisés pour la Programmation Linguistique et permet d'identifier l'intégrabilité, l'extensibilité et la généricité comme trois qualités qu'il est souhaitable de fournir aux systèmes de TALN. La définition d'un modèle à objets (LEAF) et l'affinage d'un modèle d'architecture (tableau blanc) constituent deux premiers éléments de réponse au problème de l'intégration. Une première approche de la généricité et de l'extensibilité est également présentée avec une expérience de réingéniérie du langage LT. La seconde partie approfondit les problèmes de généricité et les illustre avec la définition d'un langage original de représentation linguistique (DECOR). L'introduction de protocoles internes et externes permet de rendre ce langage particulièrement générique et dynamique. La troisième partie fait état de la réingériérie de deux langages spécialisés (ATEF et ROBRA). Cette expérience permet d'introduire une grande extensibilité dans les moteurs de ces langages par une programmation par objets et protocoles. Une question transverse à ces trois parties concerne l'identification des limites et des écueils liés à la recherche de l'intégrabilité, de l'extensibilité et de la généricité. Ces difficultés viennent principalement de la complexité croissante des protocoles adéquats, dont la maîtrise pourrait rapidement échapper au développeur si l'on n'y prend garde.

This thesis deals with a complete analysis of real data in free to play multiplayer games. The analysis is based on the methodology CRISP-DM using GUHA method and system LISp-Miner. The goal is defining player churn in pool from Geewa ltd.. Practical part show the whole process of knowledge discovery in databases from theoretical knowledge concerning player churn, definition of player churn, across data understanding, data extraction, modeling and finally getting results of tasks. In thesis are founded hypothesis depending on various factors of the game.

This diploma thesis covers KDD analysis of data and options of presentation of KDD results on Web. The paper is divided into three main sections, which follow the whole process of this thesis. In the first section are mentioned theoretical basics needed for understanding of discussed problem. In this section are described notions data matrix and domain knowledge, concept of CRISP-DM methodology, GUHA method, system LISp-Miner and implementation of GUHA method in LISp-Miner including description of core procedures 4ft-Miner and CF-Miner. The second section is dedicated to the first goal of this paper. It briefly summarizes analysis made during pre-analysis phase. Then is described process of analysis of domain knowledge in a given data set. The third part focuses on the second goal of this thesis, which is problem of presentation of KDD results on Web. This section covers brief theoretical basis for used technologies. Then is described development of export script for automatic generation of website from results found using LISp-Miner system including description of structure of the output and recommendations for work in LISp-Miner system.

Dissertation thesis deals with development and implementation of Business Intelligence (BI) solutions for Small and Medium Sized Enterprises (SME) in the Czech Republic. This thesis represents climax of author's up to now effort that has been put into completing a methodological model for development of this kind of applications for SMEs using self-owned skills and minimum of external resources and costs. This thesis can be divided into five major parts. First part that describes used technologies is divided into two chapters. First chapter describes contemporary state of Business Intelligence concept and it also contains original taxonomy of Business Intelligence solutions. Second chapter describes two Knowledge Discovery in Databases (KDD) techniques that were used for building those BI solutions that are introduced in case studies. Second part describes the area of Czech SMEs, which is an environment where the thesis was written and which it is meant to contribute to. This environment is represented by one chapter that defines the differences of SMEs against large corporations. Furthermore, there are author's reasons why he is personally focusing on this area explained. Third major part introduces the results of survey that was conducted among Czech SMEs with support of Department of Information Technologies of Faculty of Informatics and Statistics of University of Economics in Prague. This survey had three objectives. First one was to map the readiness of Czech SMEs for BI solutions development and deployment. Second was to determine major problems and consequent decisions of Czech SMEs that could be supported by BI solutions and the third objective was to determine top factors preventing SMEs from developing and deploying BI solutions. Fourth part of the thesis is also the core one. In two chapters there is the original Methodology for development and deployment of BI solutions by SMEs described as well as other methodologies that were studied. Original methodology is partly based on famous CRISP-DM methodology. Finally, last part describes particular company that has become a testing ground for author's theories and that supports his research. In further chapters it introduces case-studies of development and deployment of those BI solutions in this company, that were build using contemporary BI and KDD techniques with respect to original methodology. In that sense, these case-studies verified theoretical methodology in real use.

The diploma thesis deals with synthetic data, selected approaches to their generation together with a practical task of data generation. The goal of the thesis is to describe the selected approaches to data generation, capture their key advantages and disadvantages and compare the individual approaches to each other. The practical part of the thesis describes generation of synthetic data for teaching knowledge discovery using databases. The thesis includes a basic description of synthetic data and thoroughly explains the process of their generation. The approaches selected for further examination are random data generation, the statistical approach, data generation languages and the ReverseMiner tool. The thesis also describes the practical usage of synthetic data and the suitability of each approach for certain purposes. Within this thesis, educational data Hotel SD were created using the ReverseMiner tool. The data contain relations discoverable with SD (set-difference) GUHA-procedures.