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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/3523

Title: Research on Business Process Prediction and Simulation Using Event Log Analysis Methods
Other Titles: Verslo procesų prognozavimo ir imitavimo taikant sisteminių įvykių žurnalų analizės metodus tyrimas
Authors: Savickas, Titas
Issue Date: 2017
Publisher: VGTU leidykla „Technika“
Citation: Savickas, T. 2017. Research on Business Process Prediction and Simulation Using Event Log Analysis Methods: Doctoral Dissertation. Vilnius: Technika, 124 p.
Abstract: Business process (BP) analysis is one of the core activities in organisations that lead to improvements and achievement of a competitive edge. BP modelling and simulation are one of the most widely applied methods for analysing and improving BPs. The analysis requires to model BP and to apply analysis techniques to the models to answer queries leading to improvements. The input of the analysis process is BP models. The models can be in the form of BP models using industry-accepted BP modelling languages, mathematical models, simulation models and others. The model creation is the most important part of the BP analysis, and it is both time-consuming and costly activity. Nowadays most of the data generated in the organisations are electronic. Therefore, the re-use of such data can improve the results of the analysis. Thus, the main goal of the thesis is to improve BP analysis and simulation by proposing a method to discover a BP model from an event log and automate simulation model generation. The dissertation consists of an introduction, three main chapters and general conclusions. The first chapter discusses BP analysis methods. In addition, the process mining research area is presented, the techniques for automated model discovery, model validation and execution prediction are analysed. The second part of the chapter investigates the area of BP simula-tion. The second chapter of the dissertation presents a novel method which automatically discovers Bayesian Belief Network from an event log and, furthermore, automatically generates BP simulation model. The discovery of the Bayesian Belief Network consists of three steps: the discovery of a directed acyclic graph, generation of conditional probability tables and their combination. The BP simulation model is generated from the discovered directed acyclic graph and uses the belief network inferences during the simulation to infer the execution of the BP and to generate activity data dur-ing the simulation. The third chapter presents the experimental research of the proposed network and discusses the validity of the research and experiments. The experiments use selected logs that exhibit a wide array of behaviour. The experiments are performed in order to test the discovery of the graphs, the inference of the current process instance execution probability, the predic-tion of the future execution of the process instances and the correctness of the simulation. The results of the dissertation were published in 9 scientific publica-tions, 2 of which were in reviewed scientific journals indexed in Clarivate Analytics Science Citation Index.
URI: http://dspace.vgtu.lt/handle/1/3523
ISBN: 978-609-476-076-1
Appears in Collections:Technologijos mokslų daktaro disertacijos ir jų santraukos

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