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Methodology of development and deployment of Business Intelligence solutions in Small and Medium Sized Enterprises
Rydzi, Daniel ; Jandoš, Jaroslav (advisor) ; Vlček, Radim (referee) ; Slánský, David (referee)
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.
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Knowledge base, analytical questions, LISp-Mner system and ADAMEK data
Kubín, Richard ; Rauch, Jan (advisor) ; Šimůnek, Milan (referee)
The steps associated with the analytical question solving in terms of LISp-Miner system in ADAMEK medical data are the theme of this thesis. The operating sequence of using 4ft-Miner and SD4ft-Miner procedures in ADAMEK data together with the possibility of further use of formalized background knowledge and preparing routing for automatization of the downrighted steps are the objectiv of this thesis. The summary of the basic concepts and axioms of association rules and GUHA method is the content of the theoretical part of the thesis. Operativ part starts from CRISP-DM methodology. The operating sequence enabling searching for interesting association rules in different data, that is applied on STULONG medical data afterwards in order to get instigations for it's revision, is the produce of this thesis. Used data that come from EuroMISE are concern with cardiological patients.
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Empirical comparison of systems for knowledge discovery in databases
Benešová, Kristýna ; Berka, Petr (advisor) ; Šimůnek, Milan (referee)
S rostoucím množstvím shromažďovaných a ukládaných dat roste také potřeba a zájem majitelů těchto dat o využití jejich potenciálu k dalšímu rozhodování. Proto se vyvíjí nové přístupy a způsoby vycházející z informatiky, statistiky a oblasti strojového učení, které se této potřebě snaží vyhovět. Cílem této diplomové práce je uvést proces dobývání znalostí dat z databází na medicínských datech Tinnitus a představit systémy LISp-Miner a Weka, které daný proces podporují. Obsahem teoretické části diplomové práce je shrnutí základních charakteristik a přístupů procesu dobývání znalostí. Praktická část diplomové práce je věnována realizaci celého procesu v jednotlivých krocích. V samotném kroku modelování jsou využity již zmíněné systémy akademické LISp-Miner a Weka. Poslední část praktické části práce patří prezentaci dosažených výsledků a vlastnímu zhodnocení systémů.
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Knowledge Processing within the GUHA Method
Šťastný, Daniel ; Rauch, Jan (advisor) ; Kliegr, Tomáš (referee)
This study presents an introduction into the data-mining methodology CRISP-DM (CRoss-Industry Standard Process for Data Mining). It provides a fundamental description of association rules and the GUHA method (General Unary Hypotheses Automaton) with related 4ft-Miner, SD4ft-Miner and Action Rules. The examples are shown on real data. Sequentially the study describes the role of the domain knowledge and the project SEWEBAR (SEmantic WEb and Analytical Reports) held at UEP. The practical output of this work is the XML Schema definition for the markup language BKEF (Background Knowledge Exchange Format) designed within the SEWEBAR and the transformation file programmed in the XSL ensuring visualization of the content of any BKEF file.
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Clickstream Analysis
Kliegr, Tomáš ; Rauch, Jan (advisor) ; Berka, Petr (referee)
Thesis introduces current research trends in clickstream analysis and proposes a new heuristic that could be used for dimensionality reduction of semantically enriched data in Web Usage Mining (WUM). Click-fraud and conversion fraud are identified as key prospective application areas for WUM. Thesis documents a conversion fraud vulnerability of Google Analytics and proposes defense - a new clickstream acquisition software, which collects data in sufficient granularity and structure to allow for data mining approaches to fraud detection. Three variants of K-means clustering algorithms and three association rule data mining systems are evaluated and compared on real-world web usage data.
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