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Data analysis of web prezentation in a software company
Henčelová, Katarína ; Říhová, Zora (advisor) ; Lalinský, Jan (referee)
This thesis looks into problematic of data originating from visits of a company websites. It is partly devoted to web analytics and Business Intelligence. The aim was to create a tool for transforming and saving these data, their analysis and reporting. The product of this thesis was created for a management of a company specifically by their requests. It was made to support managerial decisions in creating a successful web strategy.
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Data mining applications in business practice
Trávníček, Petr ; Pour, Jan (advisor) ; Svatoš, Oleg (referee)
Throughout last decades, knowledge discovery from databases as one of the information and communicaiton technologies' disciplines has developed into its current state being showed increasing interest not only by major business corporates. Presented diploma thesis deals with problematique of data mining while paying prime attention to its practical utilization within business environment. Thesis objective is to review possibilities of data mining applications and to decompose implementation techniques focusing on specific data mining methods and algorithms as well as adaptation of business processes. This objective is subject of theoretical part of thesis focusing on principles of data mining, knowledge discovery from databases process, data mining commonly used methods and algorithms and finally tasks typically implemented in this domain. Further objective consists in presenting data mining benefits on the model example that is being displayed in the practical part of the thesis. Besides created data mining models evalution, practical part contains also design of subsequent steps that would enable higher efficiency in some specific areas of given business. I believe previous point together with characterization of knowledge discovery in databases process to be considered as the most beneficial one's of the thesis.
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Using data mining to manage an enterprise.
Prášil, Zdeněk ; Pour, Jan (advisor) ; Novotný, Ota (referee)
The thesis is focused on data mining and its use in management of an enterprise. The thesis is structured into theoretical and practical part. Aim of the theoretical part was to find out: 1/ the most used methods of the data mining, 2/ typical application areas, 3/ typical problems solved in the application areas. Aim of the practical part was: 1/ to demonstrate use of the data mining in small Czech e-shop for understanding of the structure of the sale data, 2/ to demonstrate, how the data mining analysis can help to increase marketing results. In my analyses of the literature data I found decision trees, linear and logistic regression, neural network, segmentation methods and association rules are the most used methods of the data mining analysis. CRM and marketing, financial institutions, insurance and telecommunication companies, retail trade and production are the application areas using the data mining the most. The specific tasks of the data mining focus on relationships between marketing sales and customers to make better business. In the analysis of the e-shop data I revealed the types of goods which are buying together. Based on this fact I proposed that the strategy supporting this type of shopping is crucial for the business success. As a conclusion I proved the data mining is methods appropriate also for the small e-shop and have capacity to improve its marketing strategy.
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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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Data mining from switchboard
BUMBA, Tomáš
Nowadays is the system performance of computing systems on such a sufficient level, so there are stored up large databases, but those are useless without using proper software tools. One of those computing systems is as well a central switchboard and its database will be scanned. It consists of hundreds of phone calls, realized throught switchboard, within and without company´s area. The target of following thesis is to discover unapparent relations in the database of central switchboard. And institute those relations into patterns of human relation.
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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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Aplikace Business Intelligence v telekomunikačním sektoru
Višňová, Marika ; Novotný, Ota (advisor) ; Slánský, David (referee)
Čtenář bakalářské práce z kontextu pochopí logickou souvztažnost mezi děním na trhu telekomunikačního sektoru a potřebou orientace společnosti na zákazníka a tím i na Churn management. V práci jsou popsány principy a komponenty Business Intelligence a dolování dat, které tvoří technologický základ Churn managementu. V poslední kapitole, která je věnována samotnému Churn managementu, je ukázáno jak probíhá vytvoření prediktivního modelu dle metodiky CRISP-DM, což by mělo alespoň zprostředkovaně přiblížit Churn management v praxi.
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