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Deep Learning for Predictive Process Behavior

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dc.contributor.author Hnin, Thuzar
dc.contributor.author Oo, Khine Khine
dc.date.accessioned 2019-07-03T08:08:37Z
dc.date.available 2019-07-03T08:08:37Z
dc.date.issued 2018-02-22
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/322
dc.description.abstract Today’s many modern organizations, to get competitive advantages, have been already implemented business process management (BPM). However, as part of a larger business process management initiative, especially predictive business process monitoring and continuous optimization of business process are still challenging for companies. Predictive business process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. In existing work, there are a lot of proposed methods to predict process behaviors. Still, deep learning (DL), a very hot research area, has been blooming for applying in predictive process behavior. Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. Therefore, this paper aims to propose new deep learning using LSTM Neural Network for predictive business process behaviors by taking into account process metrics. en_US
dc.language.iso en en_US
dc.publisher Sixteenth International Conferences on Computer Applications(ICCA 2018) en_US
dc.subject Deep Learning en_US
dc.subject Predictive Process Monitoring en_US
dc.title Deep Learning for Predictive Process Behavior en_US
dc.type Article en_US

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