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Mobile reporting
Irišek, Michael ; Kerol, Valeria (advisor) ; Novotný, Ota (referee)
Primary goal of this Bachelor thesis is to analyse chosen pairs of mobile devices and reporting applications in order to find the most suitable combination for purposes of mobile reporting. This analysis takes place in pursuance of mapping current situation on the market of appropriate mobile applications and for facilitating the choice of suitable mobile reporting solutions for potential customers. For achieving the primary goal, the author used multicriterial selection. Secondary goal is familiarizing the reader with the issues of Business Intelligence, reporting and mobile devices. The global market was analysed on basis of presented knowledge for the purpose of choosing the right combinations for further testing. Furthermore, the thesis determines evaluated criteria later used for testing purposes. Thesis informs about this process and analyses the results, therefore completing its primary goal.
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Business Intelligence trends
Meloun, Jaroslav ; Kerol, Valeria (advisor) ; Novotný, Ota (referee)
The main goal and contribution of this Bachelor thesis is to analyse and compare Business Intelligence (BI) solutions according to the actual trends in order to help a potential customer to select a suitable BI solution. Firstly, the minor goals are achieved: the basic theoretical knowledge about BI (definition of BI, a general architecture and components of BI) is provided to a reader, actual BI trends are identified and a current BI market is analysed. Next, the criteria are set and the analysis and the comparison of selected BI solutions are performed. Finally, there is a final evaluation in the form of tables. Also, strengths and weaknesses of each analysed solution are identified based on the performed analysis and the comparison.
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Machine learning in the field of Big Data
Šimánek, Michal ; Kerol, Valeria (advisor) ; Novotný, Ota (referee)
This bachelor's thesis devotes to the field of machine learning in Big Data. The main aim is to map and evaluate current situation of machine learning in Big Data, select and compare the most used machine learning libraries in Apache Spark tool and provide guide, how to implement algorithms of selected libraries. Theoretical part consists of explaining concept of Big Data, tools Apache Hadoop and Apache Spark, machine learning and decribes most used machine learning libraries in the Apache Spark tool along with comparsion metrics. Practical part is oriented to implementation of algorithms of selected libraries, writing the guide for implementation and according to outcomes and implementations comparing selected libraries from different views. Contribution of this thesis is to introduce machine learning problematics in Big Data, describe most used machine learning libraries and compare selected libraries with providing guide how to implement their algorithms.
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Integration of Big Data and data warehouse
Kiška, Vladislav ; Novotný, Ota (advisor) ; Kerol, Valeria (referee)
Master thesis deals with a problem of data integration between Big Data platform and enterprise data warehouse. Main goal of this thesis is to create a complex transfer system to move data from a data warehouse to this platform using a suitable tool for this task. This system should also store and manage all metadata information about previous transfers. Theoretical part focuses on describing concepts of Big Data, brief introduction into their history and presents factors which led to need for this new approach. Next chapters describe main principles and attributes of these technologies and discuss benefits of their implementation within an enterprise. Thesis also describes technologies known as Business Intelligence, their typical use cases and their relation to Big Data. Minor chapter presents main components of Hadoop system and most popular related applications. Practical part of this work consists of implementation of a system to execute and manage transfers from traditional relation database, in this case representing a data warehouse, to cluster of a few computers running a Hadoop system. This part also includes a summary of most used applications to move data into Hadoop and a design of database metadata schema, which is used to manage these transfers and to store transfer metadata.
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The analysis of cloud-based Business Intelligence solutions for SMEs
Slavětínský, Radek ; Novotný, Ota (advisor) ; Kerol, Valeria (referee)
The thesis is focused on the analysis of presently offered products supporting Business Intelligence (BI) which are affordable for small and medium-sized enterprises (SMEs). Current BI solutions available to SMEs are mostly offered via Cloud computing, specifically in the form of Software as a Service (SaaS) as it requires low initial acquisition costs. The objectives of this thesis are to analyse the work in applications for BI in cloud that can be used by SMEs and to analyse in detail the comparison the worldwide extended reporting tools distributed as SaaS in the lower price category. The theoretical part provides a description of the Cloud computing and the BI system. In the practical part are selected following products: IBM Watson Analytics, Qlik Sense Cloud, Zoho Reports, Tableau Public and Microsoft Power BI. Practical testing of these applications was based on evaluation of the selected metrics with weights calculated by using the Fuller's triangle. Analyses and the information form the basis for comparison of selected applications. The contribution of this thesis is in discovering the strengths and weaknesses of these BI solutions. The output of this thesis can be used as a source for the selection of BI applications for SMEs.
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Apache Hadoop as analytics platform
Brotánek, Jan ; Novotný, Ota (advisor) ; Kerol, Valeria (referee)
Diploma Thesis focuses on integrating Hadoop platform into current data warehouse architecture. In theoretical part, properties of Big Data are described together with their methods and processing models. Hadoop framework, its components and distributions are discussed. Moreover, compoments which enables end users, developers and analytics to access Hadoop cluster are described. Case study of batch data extraction from current data warehouse on Oracle platform with aid of Sqoop tool, their transformation in relational structures of Hive component and uploading them back to the original source is being discussed at practical part of thesis. Compression of data and efficiency of queries depending on various storage formats is also discussed. Quality and consistency of manipulated data is checked during all phases of the process. Fraction of practical part discusses ways of storing and capturing stream data. For this purposes tool Flume is used to capture stream data. Further this data are transformed in Pig tool. Purpose of implementing the process is to move part of data and its processing from current data warehouse to Hadoop cluster. Therefore process of integration of current data warehouse and Hortonworks Data Platform and its components, was designed
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Analysis of the Business Intelligence market
Nguyen Van, Duc ; Kerol, Valeria (advisor) ; Novotný, Ota (referee)
The main goal and contribution of this Bachelor thesis is the selection and recommendation of a suitable Business Intelligence (BI) solution for a specific enterprise consistent with the current conditions in the enterprise and the set criteria. To achieve this goal, the thesis will be divided into two big sections. The first section, the theoretical one, will explain the term Business Intelligence, its history, architecture and main components to the reader. After that, I will analyse the current BI market and provide a short summary of selected BI trends which are important from the perspective of a BI user. The practical part is dealing with the actual selection of the suitable BI solution for a specific enterprise (PTT Global s.r.o.). First of all, I will set the criteria and the weight of these criteria with regards to the specific requirements of business management. Based on these criteria, the chosen BI solutions will be thoroughly analysed and compared. The result will be the most suitable solution for the selected enterprise.
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