Business Process Event Log Transformation into Bayesian Belief Network

Business process (BP) mining has been recognized in business intelligence and reverse engineering fields because of the capabilities it has to discover knowledge about the implementation and execution of BP for analysis and improvement. Existing business knowledge extraction solutions in process mining context requires repeating analysis of event logs for each business knowledge extraction task. The probabilistic modelling could allow improved performance of BP analysis. Bayesian belief networks are a probabilistic modelling tool and the paper presents their application in BP mining. The paper shows that existing process mining algorithms are not suited for this, since they allow for loops in the extracted BP model that do not really exist in the event log,and presents a custom solution for directed acyclic graph extraction. The paper presents results of a synthetic log transformation into Bayesian belief network showing possible application in business intelligence extraction and improved decision support capabilities

Verslo procesų prognozavimo ir imitavimo taikant sisteminių įvykių žurnalų analizės metodus tyrimas

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

Rational Groupthink

We study how long-lived rational agents learn from repeatedly observing a private signal and each others' actions. With normal signals, a group of any size learns more slowly than just four agents who directly observe each others' private signals in each period. Similar results apply to general signal structures. We identify rational groupthink---in which agents ignore their private signals and choose the same action for long periods of time---as the cause of this failure of information aggregation