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Filter interesting rules in EasyMiner system
Duben, Přemysl Václav ; Vojíř, Stanislav (advisor) ; Zeman, Václav (referee)
Postprocessing is like data preparation one of the most challenging tasks in data mining that users must deal with. It is desirable to simplify it so that the path to results is as fast and efficient as possible. The extension of the EasyMiner research project to filter the association rules by similarity to the knowledge-based rules, should be helped in this respect, which is the subject of this diploma thesis. The objectives were accomplished by a detailed analysis of the default state of EasyMiner in conjunction with a thoroughly thought-out implementation proposal without increasing the demands on the server or user of the application. An analysis of general practices and the author's deep knowledge of Internet application issues served to do this. The future deployment of this extension to the EasyMiner infrastructure will benefit from a clearer and more efficient work with the Knowledge base part, where it will no longer be necessary to evaluate the interest in the same or similar rules and the user will be able to focus directly on the quality of the results. This thesis is divided into chapters as a detailed description of how a similar problem can be approached in any other project that works with a certain form of knowledge base. Initial input analysis with access search for comparison of the various elements passes through a description of the default application state to a specific solution design. This should be a guideline for the implementation itself and for the testing of the proposed and implemented procedures.
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Business rule learning using data mining of GUHA association rules
Vojíř, Stanislav ; Strossa, Petr (advisor) ; Pour, Jan (referee) ; Kouba, Zdeněk (referee) ; Gregor, Jiří (referee)
In the currently highly competitive environment, the information systems of the businesses should not only effectively support the existing business processes, but also have to be dynamically adaptable to the changes in the environment. There are increasing efforts at separation of the application and the business logic in the information system. One of the appropriate instruments for this separation is the business rule approach. Business rules are simple, understandable rules. They can be used for the knowledge externalization and sharing also as for the active control and decisions within the business processes. Although the business rule approach is used for almost 20 years, the various specifications and practical applications of business rules are still a goal of the active research. The disadvantage of the business rule approach is great demands on obtaining of the rules. There has to be a domain expert, who is able to manually write them. One of the problems addressed by the current research is the possibility of (semi)automatic acquisition of business rules from the different resources - unstructured documents, historical data etc. This dissertation thesis addresses the problem of acquisition (learning) of business rules from the historical data of the company. The main objective of this thesis is to design and validate a method for (semi)automatic learning of business rules using the data mining of association rules. Association rule are a known data mining method for discovering of interesting relations hidden in the data. Association rules are comprehensible and explainable. The comprehensibility of association rules is suitable for the use of them for learning of business rules. For this purpose the user can use not only simple rules discovered using the algorithm Apriori or FP-Growth, but also more complex association rules discovered using the GUHA method. Within this thesis is used the procedure 4ft-Miner implemented in the data mining system LISp Miner. The first part of this thesis contains the description of the relevant topics from the research of business rules and association rules. Business rules is not a name of one specification of standard but rather a label of the approach to modelling of business logic. As part of the work there is defined a process of selection of the most appropriate specification of business rules for the selected practical use. Consequently, the author proposed three models of involving of data mining of association rules into business rule sets. These models contain also the definition of a model for the transformation of GUHA association rules in the business rules for the system JBoss Drools. For the possibility of learning of business rules using the data mining results from more than one data set, the author proposed a knowledge base. The knowledge base is suitable for the interconnection of business rules and data mining of association rules. From the perspective of business rules the knowledge base is a term dictionary. From the perspective of data mining the knowledge base contains some background knowledge for data preprocessing and preparation of classification models. The proposed models have been validated using practical implementations in the systems EasyMiner (in conjunction with JBoss Drools) and Erian. The thesis contains also a description of two model use cases based on real data from the field of marketing and the field of health insurance.
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Creation of an interactive Help for web application EesyMiner
Hanišák, Petr ; Vojíř, Stanislav (advisor) ; Chudán, David (referee)
The aim of this work is to create an interactive Help system for a web interface called EasyMiner which is used for mining association rules from databases. The research of existing types of software Help contributed to the solution of the problem and brought perspective on what solutions of Help are suitable for different kinds of software. Furthermore, there was made an analysis of EasyMiner which is focused on its functioning and its current state of user assistance. The analysis also addresses specific areas and situations for which help is most needed. The practical part deals with the design and subsequent implementation of the Help system. Under the design part it focuses on selection of appropriate technologies and the way it will be implemented in the EasyMiner system including the form of its presentation. This is followed by a description of the actual implementation of the designed Help system in terms of code and user interface. The result is a Help system based on web technologies, which is implemented directly in the user interface of EasyMiner. The main benefit of this Help system is helping inexperienced users and students, who use the application in teaching, with orientation in its environment.
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Business rules editor
Duben, Přemysl Václav ; Vojíř, Stanislav (advisor) ; Dudáš, Marek (referee)
The main focus of this Bachelor thesis is on the design and implementation of business rules editor usable in a research project EasyMiner. This editor should help nontechnical expert to create business rules in simplified format of structured language SBVR. To achieve a goal there is used the knowledge of Web Development issue in conjunction with a detailed research of similar tools. Within this thesis are tested two ways of control with a clear appreciation their usability and with suggestion for future development. Benefit of this thesis should be particularly realistic deployment within the infrastructure of EasyMiner project, which is supposed to be a tentative alternative editor of founded association rules that expand the possibilities of association rules data-mining on progressively created knowledge base in business rules format. This thesis is divided into theoretical and analytical-implementation part. In the theoretical part the reader is familiar with the essential requirements, keywords and relations, research of similar tools as well as with literature review. In the second part is focused attention on comprehensive analysis of editor development with a focus on modern standards to achieve the best result.
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