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Analysis of Current Areas of Data Warehouse Solutions
Hník, Pavel ; Pour, Jan (advisor) ; Dvořáková, Dana (referee)
This thesis analyzes various factors of impact on current data warehouse solutions. It is structured along three main sections. The first section dissects current issues faced by data warehouses. The second section focuses on an analysis of how the market for data warehouse solutions has developed; within this context, it also mentions other, related markets. The last section is devoted to current trends in the area of data warehouses and Business Intelligence. While this work focuses on data warehouses proper, the topic is closely interconnected with the overarching category of Business Intelligence, which is why a suitable degree of discussion also of this area appeared to be in order. This paper does not seek to provide advice as to which specific solutions management should choose for their business, nor to serve as a manual on how exactly to implement a data warehouse so as to avoid potential issues. Rather, this thesis attempts to provide a comprehensive and transparent overview of the factors which have impact on today's data warehouse solutions. The rationale behind this thesis is to draw special attention to the key influences on data warehouse solutions at this point in time and to give an informed estimate of their likely future development.
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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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Empirical comparison of free software suites for knowledge discovery from data
Kasík, Josef ; Berka, Petr (advisor) ; Rauch, Jan (referee)
Both topic and main objective of the diploma thesis is a comparison of free data mining suites. Subjects of comparison are six particular applications developed under university projects as experimental tools for data mining and mediums for educational purposes. Criteria of the comparison are derived from four general aspects that form the base for further analyses. Each system is evaluated as a tool for handling real-time data mining tasks, a tool supporting various phases of the CRISP-DM methodology, a tool capable of practical employment on certain data and as a common software system. These aspects bring 31 particular criteria for comparison, evaluation of whose was determined by thorough analysis of each system. The results of comparison confirmed the anticipated assumption. As the best tool the Weka data mining suite was evaluated. The main advantages of Weka are high number of machine learning algorithms, numerous data preparation tools and speed of processing.
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Data warehouses -- main principles, concepts and methods, tools, applications, design and building of data warehouse solution in real company
Mašek, Martin ; Jelínek, Jiří (advisor) ; Novák, Viktor (referee)
The main goal of this thesis is to summarize and introduce general theoretical concepts of Data Warehousing by using the systems approach. The thesis defines Data Warehousing and its main areas and delimitates Data Warehousing area in terms of higher-level area called Business Intelligence. It also describes the history of Data Warehousing & Business Intelligence, focuses on key principals of Data Warehouse building and explains the practical applications of this solution. The aim of the practical part is to perform the evaluation of theoretical concepts. Based on that, design and build Data Warehouse in environment of an existing company. The final solution shall include Data Warehouse design, hardware and software platform selection, loading with real data by using ETL services and building of end users reports. The objective of the practical part is also to demonstrate the power of this technology and shall contribute to business decision-making process in this company.
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Practical Use of Data Mining Technologies
Uhlíř, Radek ; Pour, Jan (advisor) ; Zajíc, Ján (referee)
This bachelor's thesis maps available technologies of extracting knowledge from the raw data. These methods are globally known as Data Mining. Some of these methods are implemented in the second part - proof of concept of Data mining support in sales department. The aim of this work is to identify and implement suitable technologies for answering analytical questions and getting knowledge from data owned by business companies. It should help to improve and optimize business processes and resource utilization. Customer segmentation support and association rules identification are also expected. In the second part are identified possible weaknesses and problems during the process of implementation and deployment of these systems. The work should propose optimal methods of solving these problems or at least modifications in process of implementation to eliminate some vulnerability. The work is divided into two parts -- first is theoretical and maps available methods and second part is about implementation of project in pharmaceutical company. This solution was built using Microsoft SQL Server platform.
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Web Analytics: Identification of new trends
Slavík, Michal ; Kliegr, Tomáš (advisor) ; Nekvasil, Marek (referee)
The goal of this thesis is to identify the main trends in the field of tools used to analyse web traffic. The necessary theoretical background is extracted from relevant literature and field research is chosen to gain knowledge of practitioners. Following trends have been identified: a growth in demand for Web Analytics software, an increasing interest in Web Analytics courses, an enlargment of measuring Web 2.0 and social networks, use of semantic information as the most fruitful section of academic research. The thesis also presents the main techniques of Web Usage Mining: association rules, sequential patterns, and clustering. A section about query categorization is also included. According to the field research, practitioners express most interest in clustering. The first two chapters present Web Analytics in general and introduce the main aspects of current applications. The third chapter covers theoretical research, the fifth one presents results of the field research. The fourth chapter raises the point that terminology of Web Analytics is not unified.
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Data Mining of Macroeconomic Data
Lang, Lukáš ; Berka, Petr (advisor) ; Marek, Luboš (referee)
The theme of my work is the Data Mining (DM) of Macroeconomic Data. The purpose of this work is to use DM methods for analysis of macroeconomic fundamentals of selected countries of the Western Europe and the U.S.A. between years 1961-1989 and to compare the DM methods with statistical methods. For the statistical analysis, I used EViews and MS-Office Excel, for the DM I used LISp-Miner. The structure of the work is as follows: in the theoretical part I define the analysed indicators and their relations with respect to the history of analysed period. Then are specified chosen statistical methods also with the reason for choice. The last chapter of the theoretical part describes the data mining. In the practical part I describe the problems which I solved, data collecting and preparation, use of the statisitical methods and DM methods and results obtained. The enlightenment lies in conclusion. I thank my supervisor, Prof. Ing. Petr Berka, CSc. for DM meditations and important suggestions. I thank my colleagues, Ing. Vojtech Menzl, MSc and Mgr. Jana Závacká for critique of the statistical methods. I thank the living members of my family for patience.
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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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Model pro ohodnocení bonity klienta v pojišťovně
Píška, Vladimír ; Novotný, Ota (advisor) ; Slánský, David (referee)
Diplomová práce se zabývá problematikou hodnocení bonity klienta v české komerční pojišťovně. Skládá se ze dvou hlavních logických celků ? přípravy teoretického modelu bonity klienta a jeho praktického ověření na reálných datech jedné české pojišťovny. Příprava modelu bonity klienta se přidržuje postupu popsaného v metodice CRISP-DM. Postupně jsou prozkoumány současné způsoby sledování bonity klientů v českém bankovním i nebankovním sektoru a je rozebrán způsob určování bonity klienta v amerických pojišťovnách. Následuje samotné sestavování modelu bonity klienta v pojišťovně. Nejdříve jsou nalezeny oblasti ke sledování a z těchto oblastí jsou vybrány vhodné ukazatele bonity klienta. Přípravu modelu uzavírá nastavení vah u jednotlivých ukazatelů a popis sledovaných kategorií bonity klienta. Druhý logický celek se zabývá aplikací připraveného modelu bonity klienta v praxi. Popsána je fyzická architektura řešení, příprava datové základny, použitá skóringová aplikace a převedení modelu bonity klienta do této aplikace. Dalšími popsanými kroky jsou testování modelu na vzorku dat a na kompletním portfoliu klientů spolupracující pojišťovny. Výsledky jsou analyzovány a zobrazeny v grafech. Poté jsou obdržené výsledky porovnávány s očekávanými výsledky. Diplomová práce končí diskuzí k využití bonity klienta v reálných procesech pojišťovny.
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