Abstract The information age is characterized by ever-growing amounts of data surrounding us. By reproducing this data into usable knowledge we can start moving toward the knowledge age. Data mining is the science of transforming measurable information into usable knowledge. During the data mining process, the measurements pass through a chain of sophisticated transformations in order to acquire knowledge. Furthermore, in some applications the results are implemented as software solutions so that they can be continuously utilized. It is evident that the quality and amount of the knowledge formed is highly dependent on the transformations and the process applied. This thesis presents an application independent concept that can be used for managing the data mining process and implementing the acquired results as software applications. The developed concept is divided into two parts – solution formation and solution implementation. The first part presents a systematic way for finding a data mining solution from a set of measurement data. The developed approach allows for easier application of a variety of algorithms to the data, manages the work chain, and differentiates between the data mining tasks. The method is based on storage of the data between the main stages of the data mining process, where the different stages of the process are defined on the basis of the type of algorithms applied to the data. The efficiency of the process is demonstrated with a case study presenting new solutions for resistance spot welding quality control. The second part of the concept presents a component-based data mining application framework, called Smart Archive, designed for implementing the solution. The framework provides functionality that is common to most data mining applications and is especially suitable for implementing applications that process continuously acquired measurements. The work also proposes an efficient algorithm for utilizing cumulative measurement data in the history component of the framework. Using the framework, it is possible to build high-quality data mining applications with shorter development times by configuring the framework to process application-specific data. The efficiency of the framework is illustrated using a case study presenting the results and implementation principles of an application developed for predicting steel slab temperatures in a hot strip mill. In conclusion, this thesis presents a concept that proposes solutions for two fundamental issues of data mining, the creation of a working data mining solution from a set of measurement data and the implementation of it as a stand-alone application.

When using Business Intelligence (BI), organizations can improve decision-making bycompiling, understanding and utilizing the data held by the organization. Self-serviceBusiness Intelligence (SSBI) has emerged as a new focus within BI and aims to make BI tools available to business users, and relieve IT-experts involvement of in ad hoc reporting andanalysis. The aim of this research is to examine challenges of casual users being self-reliant when using SSBI tools, and the way they are using them in the data mining process. This wasdone through a qualitative study, interviewing seven individuals from different user groups: casual users, power user and IT-experts. From the results it appears that most casual users are not sufficiently self-reliant in the data mining process using SSBI. The visualization is theprime area for SSBI, which most casual users manage themselves if the data and dashboards are pre-defined for them. SSBI is becoming increasingly more common, which leads to moreand more casual users with increased experience who need to be able to dig out their own data for interpretation and analysis. Yet, without additional knowledge, such as data knowledge or SQL skill, casual users are in need of support when it comes to more complex operations thanad hoc analysis and reporting, still creating a request-response relationship to power users and IT-experts. The major challenges, limiting casual users from being self-reliant, are: not sufficiently user-friendly tools, poor data definition and lack of data knowledge, limited data access, indigent validation of reports and lastly, inadequate education.

This master thesis deals with automation process of a data mining in the LISp-Miner program. The aim of this thesis is to create an automated process that analyzes analytical questions in the data about traffic accidents in the Czech Republic using a LMCL scripting language and LM Exec module. Theoretical part of thesis describes the process of knowledge discovery in databases and most widely used methodology. It also describes the relevant topics for the work with LISp-Miner. The practical part is focused on description of traffic accidents in the Czech Republic, a description of the used data, creation and evaluation of analytical questions and especially a description of created scripts. The output of the thesis is a group of scripts and manual how to use them again, so they can be reused for analysis of actual data on traffic accidents not only in the Czech Republic, if they have the same data structure.

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