Decision Support Using Belief Network Constructed from Business Process Event Log

Savickas, Titas; Vasilecas, Olegas

doi:10.15388/informatica.2017.146

Cited by 2 publications

(1 citation statement)

References 17 publications

(12 reference statements)

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“…The purpose of this section is to review the state-of-the-art in existing BP analysis methods and identify the drawbacks and gaps in existing research. The contents of this chapter are based on previously published content by the author in (Savickas & Vasilecas, 2017;Vasilecas, Savickas, Normantas, Vysockis, & Kalibatiene, 2015). BPs are not easy to define and, based on perspective, can have many different properties.…”

Section: Investigation Of Methods For Business Process Analysismentioning

confidence: 99%

Research on Business Process Prediction and Simulation Using Event Log Analysis Methods

Savickas¹

2017

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Vadovas prof. dr. Olegas VASILECAS (Vilniaus Gedimino technikos universitetas, informatikos inžinerija -07T).Vilniaus Gedimino technikos universiteto Informatikos inžinerijos mokslo krypties disertacijos gynimo taryba: Pirmininkas prof. dr. Dalius MAŽEIKA (Vilniaus Gedimino technikos universitetas, informatikos inžinerija -07T). Nariai:prof. habil. dr. Antanas ČENYS (Vilniaus Gedimino technikos universitetas, informatikos inžinerija -07T), dr. Robertas DAMAŠEVIČIUS (Kauno technologijos universitetas, informatikos inžinerija -07T), prof. habil. dr. Gintautas DZEMYDA (Vilniaus universitetas, informatikos inžinerija -07T), doc. dr. Raimundas MATULEVIČIUS (Tartu universitetas, Estija, informatikos inžinerija -07T).Disertacija bus ginama viešame Informatikos inžinerijos mokslo krypties disertacijos gynimo tarybos posėdyje 2017 m. gruodžio 18 d. 10 val. Vilniaus Gedimino technikos universiteto senato posėdžių salėje.

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Section: Investigation Of Methods For Business Process Analysismentioning

confidence: 99%

Research on Business Process Prediction and Simulation Using Event Log Analysis Methods

Savickas¹

2017

Self Cite

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Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Mrowca

Gyrock

Günnemann

2021

Data Min Knowl Disc

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Many systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.

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Decision Support Using Belief Network Constructed from Business Process Event Log

Cited by 2 publications

References 17 publications

Research on Business Process Prediction and Simulation Using Event Log Analysis Methods

Research on Business Process Prediction and Simulation Using Event Log Analysis Methods

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

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