This diploma thesis deals with the problem of automatic alerting on Microsoft SQL Server 2008 platform. Thesis was elaborated with Clever Decision s.r.o. company which significantly contributed to thesis assignment. Main goal of this thesis is to provide a solution concept for absence of alerting for multidimensional data in Microsoft's product. Alerting is aimed at critical business data -- Key performance indicators - KPIs. KPIs are used to deliver enhanced business performance through Corporate Performance Management concept. These indicators need to be watched frequently. Considering that alerting solution is convenient for watching the data. Solution concept is based on market research of Business Intelligence (BI) platforms which dominates the market. Platforms are examined for their alerting capabilities. Next goal is to provide overall insight to KPI management -- creating, managing, analyzing and monitoring of KPIs. Thesis is divided into three sections. First part creates theoretical background for solution creation and describes the field of KPI management. Second part consists of market research using selected criteria. Third part is aimed at providing the solution concept through web application fixing the absence of alerting on Microsoft SQL Server 2008 platform. This thesis is primarily intended for Clever Decision company but also for various BI experts dealing with alerting problems on BI platforms. Thesis serves also as a theoretical summary of KPI management which is commonly evaded by available technical literature.

This thesis is oriented on practical usage of tools for data mining and business intelligence. Main goals are processing of source data to suitable form and test use of chosen tool on the test case. As input data I used database which was created as result of processing forms from research to verify the level of IT and economics knowledge among Czech universities. These data was modified into the form, which allows processing them via data mining tools included in Microsoft SQL Server 2008. I choose two cases for verification the potentials of these tools. First case was focused on clustering using Microsoft Clustering algorithm. Main task was to sort the universities into the clusters by comparing their attributes which was amounts of credits of each knowledge group. I had to deal with two problems. It was necessary to reduce the number of groups of subjects, otherwise there was a danger of creation too many clusters which I couldn't put the name on. Another problem was unequal value of credits in each group and this problem caused another problem with weights of these groups. Solution was at the end quite simple. I put together similar groups to bigger formation with more general category. For unequal value, I used parameter for each of new group and transform it to scale 0-5. Second case was focused on prediction task using Microsoft Logistic Regresion algorithm and Microsoft Neural Network algorithm. In this case was the goal to predict the number of presently studying students. I had a historical data from years 2001-2009. A predictive model was processed based on them and I could compare the prediction with real data. In this case, it was also necessary to transform the source data, otherwise it couldn't be processed by tested tool. Original data was placed into the view instead of table and contained not only wished objects but more types of these. For example divided by a sex. Solution was in creation of new table in database where only relevant objects for test case were placed. Last problem come up when I tried to use prediction model to predict data for year 2010 for which there wasn't real data in the table. Software reported an error and couldn't make prediction. During my research on the Microsoft technical support I find some threads which refer to similar problem, so it's possible that this is a system error whit will be fix in forthcoming actualization. Fulfillment of these cases provided me enough clues to determine abilities of these tools from Microsoft. After my former school experience with data mining tools from IBM (former SSPS) and SAS, I can recognize, if tested tools can match these software from major data mining supplier on the market and if it can be use for serious deployment.

The aim of this thesis is analysis of data of the Czech Statistical Office concerning foreign trade. At first, reader familiarize with Business Intelligence and data warehousing. Further, OLAP analysis and data mining basics are explained. In next parts the thesis deal with describing and analysis of data of foreign trade by the help of OLAP technology and data mining in MS SQL Server including selected analytical tasks implementation.

This master’s thesis concerns about the use of data mining techniques in banking, insurance and shopping centres industries. The thesis theoretically describes algorithms and methodology CRISP-DM dedicated to data mining processes. With usage of theoretical knowledge and methods, the thesis suggests possible solution for various industries within business intelligence processes.

This thesis deals with the design and implementation of a dynamic user-definable dashboard. The user will be able to define conditions dynamically, which will filter out and save only the data he needs. The application will support the changing of the condition definitions and the display of the graphs after they were created. The current implementations available on the internet are usually solutions designed to fit only one type of project and are not designed to meet general guidelines for a dashboard. The dashboard is designed for a smooth cooperation with high load databases and therefore not to slow down the whole solution.

The purpose of this work is in analytical tool supporting manager decision making. The goal was to develop a reporting system which simplifies company customer's orientation in key performance indicators. As implementation environment has been used .NET Framework 3.5 with C\# language and database server Microsoft SQL 2008 with Analysis Services extension, for web interface has been used ASP.NET.

Model View Controller (MVC) software architecture is widely spread and commonly used in application’s development. Therefore generation of data layer for the database model is able to reduce cost and time. After research on current Object Relational Mapping (ORM) tools, it was discovered that there are generating tools like Data Access Object (DAO) and Hibernate, however their usage causes problems like inefficiency and slow performance due to many connections with database and set up time. Most of these tools are trying to solve specific problems rather than generating a data layer which is an important component and the bottom layer of database centred applications. The proposed solution to the problem is an engineering approach where we have designed a tool named Generated Intelligent Data Layer (GIDL). GIDL tool generates small models which create the main data layer of the system according to the Database Model. The goal of this tool is to enable and allow software developers to work only with object without deep knowledge in SQL. The problem of transaction and commit is solved by the tool. Also filter objects are constructed for filtering the database. GIDL tool reduced the number of connections and also have a cache where to store object lists and modify them. The tool is compared under the same environment with Hibernate and showed a better performance in terms of time evaluations for the same functions. GIDL tool is beneficial for software developers, because it generates the entire data layer.

This thesis deals with Business Intelligence and its testing. It seeks to highlight the differences from the classical software testing and finally design a methodology for BI solutions testing that could be used in practice on real projects of BI companies. The aim of thesis is to design a methodology for BI solutions testing based on theoretical knowledge of Business Intelligence and software testing with an emphasis on the specific BI characteristics and requirements and also in accordance with Clever Decision's requirements and test it in practice on a real project in this company. The paper is written up on the basis of studying literature in the field of Business Intelligence and software testing from Czech and foreign sources as well as on the recommendations and experience of Clever Decision's employees. It is one of the few if not the first sources dealing with methodology for BI solutions testing in the Czech language. This work could also serve as a basis for more comprehensive methodologies of BI solutions testing. The thesis can be divided into theoretical and practical part. The theoretical part tries to explain the purpose of Business Intelligence use in enterprises. It elucidates particular components of the BI solution, then the actual software testing, various types of tests, with emphasis on the differences and specificities of Business Intelligence. The theoretical part is followed by designed methodology for BI solutions using a generic model for the BI/DW solution testing. The practical part's highlight is the description of real BI project testing in Clever Decision according to the designed methodology.

This work deals with the modifications of project MeDiMed, which ceased to meet the requirements it imposed. In the first part the main objectives, targets and methods to achieve them are defined. The second part deals with the theory that is needed to understand the issue. In the practical part, which is based on the theory is prepared SWOT analysis and the modified HOS-8 analysis of the current state, whose results are then analyzed. The next section presents design of new solution. The conclusion summarizes all the proposed solutions and knowledge that have been achieved in this work.

This work deals with the modifications of project MeDiMed, which ceased to meet the requirements it imposed. In the first part the main objectives, targets and methods to achieve them are defined. The second part deals with the theory that is needed to understand the issue. In the practical part, which is based on the theory is prepared SWOT analysis and the modified HOS-8 analysis of the current state, whose results are then analyzed. The next section presents design of new solution. The conclusion summarizes all the proposed solutions and knowledge that have been achieved in this work.