Arcabouço de classificação e escolha de algoritmos de descoberta de processos
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| Hovedforfatter: | |
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| Publication Date: | 2017 |
| Format: | Master thesis |
| Sprog: | por |
| Source: | Repositório Institucional da UFG |
| Download full: | http://repositorio.bc.ufg.br/tede/handle/tede/7607 |
Summary: | Process Mining is a recent area of research and is composed of techniques that allow the analysis and extraction of knowledge from the logs of the business processes obtained from Management Information Systems (MIS). The analyzes can be classified into three types: Process Discovery, Conformance Check and Process Improvement. With the current growth not only of quantity, but also of the types of algorithms that seek to fulfill the objectives of Process Mining, a classification that takes into account the performance of the algorithm in the various real situations of its application becomes important. The Evaluation and Comparison of the algorithms from the repository data could be done through the application of Quality Metrics or Machine Learning Techniques. This work presents a proposal of a set of Quality Metrics to allow the classification, evaluation and comparison of Process Discovery algorithms. The proposal is based on the review of algorithms and their families; the possible performance characteristics, that can be applied to any type of algorithm being tested; and in simulations of business process patterns. The results obtained by the work are promising in the sense of creating the conceptual basis and a methodology for future research to allow the construction of a framework for Evaluation and Comparison of new algorithms. |
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