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Text and Data Mining of Grey Literature for the Purpose of Scientific Research
Myška, Matěj
The paper explores the legal possibilities of users to text and data mine repositories of grey literature for scientific research without the consent of the GL repository operator and right holders of the documents contained therein. Firstly the scope of the respective copyright and sui generis database rights exceptions for scientifi c research is analyzed. Secondly the term “scientific research” and its meaning in the regulatory instruments is explored. Lastly the debated mandatory exception for TDM for scientific research is introduced.
Fulltext: idr-1037_3 -  PDF
Slides: idr-1037_1 - PDF; idr-1037_2 - PDF
Video: idr-1037_4 -  MP4
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Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand
Saxa, Branislav
This paper examines the usefulness of Google Trends data for forecasting mortgage lending in the Czech Republic. While the official monthly statistics on mortgage lending come with a publication lag of one month, the data on how often people search for mortgage-related terms on the internet are available without any lag on a weekly basis. Growth in searches for mortgages and growth in mortgages actually provided are strongly correlated. The lag between these two growth rates is two months. Evaluation of out-of-sample forecasts shows that internet search data improve mortgage lending predictions significantly. In addition to forecasting performance evaluation, an experimental indicator of restrictively tight mortgage credit standards and conditions is proposed. Nowadays many countries run bank lending surveys to monitor the tightness of bank lending standards and conditions. The proposed indicator represents a complementary tool to such a survey.
Fulltext: PDF
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Data visualizations in BI reporting tools
Brož, Ondřej ; Pour, Jan (advisor) ; Štoček, Milan (referee)
This diploma thesis deals with the Business Intelligence reporting tools and more especially with their data visualization capabilities. There are a lot of different reporting and analytic tools focusing for example on ad-hoc query, tool for creating dashboards or scorecards, visual discovery tools and advanced analytical tools for data mining etc. This work, however, is focused especially on selected enterprise reporting tools and data visualization tools as well as their possibilities of data isualizations, i.e. using different types of graphs and other possible advanced data visualizations. The Business Intelligence fundamentals together with the possible the reporting and analysis visual outputs are described at the beginning of this diploma work. The reporting itself is also putted into the context of the Business Intelligence solution. The next section is focus on the visual possibilities of data visualization in BI reporting tools. It means that I created the list of possible BI data visualizations with the detail specifications and showing examples on each of them. After this part, there are shown proper data visualizations for each of the selected BI domains, like finance, sales, marketing and human resources. This output belong to one of the main contributions of this work and also could be practically useful in the process of report creation and selection for Clever Decision's BI reporting needs. In the next chapter I compare the visual possibilities for each of the selected BI reporting tools according to Gartner. To this purpose the official documentations will be used as well as my personal experience gained from using some of selected reporting tools. In the last section there are detail views for each of two selected advanced data visualizations including a detail specification on each of them as well as his possible usage in practice and possibilities for further extension as well. The source of information in this section will be professional electronic resources focused on data visualization, design and so on.
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Business Intelligence principles and their use in questionnaire investigation
Hanuš, Václav ; Maryška, Miloš (advisor) ; Novotný, Ota (referee)
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.
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Systém předzpracování dat pro dobývání znalostí z databází
Kotinová, Hana ; Berka, Petr (advisor) ; Šimůnek, Milan (referee)
Abstract Aim of this diploma thesis was to create an aplication for data preprocessing. The aplication uses files in csv format and is useful for preparing data while solving datamining tasks. The aplication was created using the programing language Java. This text discusses problems, their solutions and algorithms associated with data preprocessing and discusses similar systems such as Mining Mart and SumatraTT. A complete aplication user guide is provided in the main part of this text.
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Dolování dat jako součást podnikového zpravodajství
Bláha, Michal ; Rydzi, Daniel (advisor) ; Studnička, Lumír (referee)
Práce se věnuje problematice ?dolování dat jako součást podnikového zpravodajství?. Dolování dat, v širším významu dobývání znalostí z databází, je součástí oblasti nazývané Business Intelligence (BI). Tento pojem lze definovat jako ?komplex procesů, aplikací a technologií IS/ICT, které téměř výlučně podporují analytické a plánovací činnosti podniku?.[1] Práce popisuje jednotlivé komponenty Business Intelligence a jejich význam. Jednou z těchto komponent může být právě dolování dat. Kapitola věnovaná této problematice obsahuje její popis a uvádím zde metody, které patří k nejdůležitějším. Práce obsahuje i kapitolu věnovanou oblasti databází, neboť tato problematika je velice blízká jejímu tématu.
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