京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22575号 / 情博第712号 / 新制||情||122(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 黒田 知宏, 教授 守屋 和幸, 教授 吉川 正俊 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This diploma thesis deals with the design of a data warehouse that stores events created in the mobile app. The goal was to design an alternative to the current solution. The thesis explains concepts, mainly data warehouse terminology, which are used in subsequent chapters. An analysis of the current solution is conducted, and as well as research of available data warehouse and ETL services. Based on the results of the analysis, a suitable new solution is chosen, implemented, and tested.

This master’s thesis deals with comparison of the standard Business Intelligence platform specifically IBM Cognos BI and BI module integrated in the ERP system QI. In the first part it defines basic concepts of this topic and describes the life cycle of business information systems with emphasis on Business Intelligence. In the next part current situation on the market of these platforms and BI tools which create management information systems is described. Both types of BI solutions are compared with concentration on standard functionality usage. It also describes their ability to analyze through using data warehouse technology, integration with third-party products and business and economic aspects.

The master‘s thesis is focused at the use of machine learning in business environment. In this thesis I describe detailed process of how I found the target prediction, this includes cleaning data and work with them.

Master`s thesis is based on the assignment of company Honeywell to create integral system for saving and management of development documentation. Designed system will introduce data warehouse with transparent display of documents being connected with development of customer’s requirements, products and tests, test runs. Solution consists of two parts: A – Requirements and Products and B – Tests, Test runs and Person, whereas Master`s thesis deals with the part B. For system creation utilities of following programming languages were used HTML, PHP, JavaScript and database system MySQL. In the first phase was designed database structure (tables and relations between them). Except that was designed structure for authorization algorithm and integral concept of whole application was created. The second phase is realization. At first are created supporting algorithms. It is servicing database by functions, uniform displaying results from database and authorized access of users to the application. Further data library for integral display data forms, based on used template system Smarty and other libraries, f.e. attribution of the files to records in SQL tables. Important part was implementation of designed database system and related programs. In Tests and Test runs there is user interface established and individual relations between them. According to assignment all fundamental programs were designed together with support instruments. Individual source files are saved in transparent structure of folders, so this structure is possible to apply on any computer, where are installed programs to web hosting. The result of Master`s thesis is then integrated progress report together with functional source codes.

Aim of master’s thesis is design of Business Intelligence solution based on non commercial technology, consideration of installation costs, estimation of economic benefits and designing of final solution of currently unsatisfactory situation in company Fortemix s.r.o.

This thesis focus on creating data warehouse and process of its implementation. The content is: Introduction into data warhousing, implemetation of data warehouse and process of its population.

An important part of knowledge of report creator is knowledge of database schema and database query language, from which the data for report are extracted. In the reporting services for database systems and Business Intelligence systems initiative arises to separate the position of database specialist from the position of reports maker. One of the solutions offers using metadata interlayer between the database schema and report. This interlayer is called the report model. Its use is not currently supported in the process of reporting, or is only very limited. The aim of this thesis is to suggest the possibility of using the report model in the process of building reports with an emphasis on the OLAP analysis.

A data warehouse (DW) is a transformation of many sources of transactional data integrated into a single collection that is non-volatile and time-variant that can provide decision support to managerial roles within an organization. For this application, the database server needs to process multiple users’ queries by joining various datasets and loading the result in main memory to begin calculations. In current systems, this process is reactionary to users’ input and can be undesirably slow. In previous studies, it was shown that a personalization scheme of a single user’s query patterns and loading the smaller subset into main memory the query response time significantly shortened the query response time. The LPCDA framework developed in this research handles multiple users’ query demands, and the query patterns are subject to change (so-called concept drift) and noise. To this end, the LPCDA framework detects changes in user behaviour and dynamically adapts the personalized smart cube definition for the group of users. Numerous data mart (DM)s, as components of the DW, are subject to intense aggregations to assist analytics at the request of automated systems and human users’ queries. Subsequently, there is a growing need to properly manage the supply of data into main memory that is in closest proximity to the CPU that computes the query in order to reduce the response time from the moment a query arrives at the DW server. As a result, this thesis proposes an end-to-end adaptive learning ensemble for resource allocation of cuboids within a a DM to achieve a relevant and timely constructed smart cube before the time in need, as a way of adopting the just-in-time inventory management strategy applied in other real-world scenarios. The algorithms comprising the ensemble involve predictive methodologies from Bayesian statistics, data mining, and machine learning, that reflect the changes in the data-generating process using a number of change detection algorithms. Therefore, given different operational constraints and data-specific considerations, the ensemble can, to an effective degree, determine the cuboids in the lattice of a DM to pre-construct into a smart cube ahead of users submitting their queries, thereby benefiting from a quicker response than static schema views or no action at all.

Motivation • Warum sollten wir Daten analysieren? Beyond Excel • Wie können wir Daten analysieren? Fallbeispiele • Welche Erkenntnisse haben wir gewonnen? • Flächenbedarf der Montage • Arbeitskräftebedarf in der Logistik • (Auto-) Korrelation