Decay Replay Mining to Predict Next Process Events

Details

• Personal Author:
  Darabi H ; Theis J
• Description:
  In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feature of event instances. In this paper, we take advantage of Petri nets as a powerful tool in modeling complex process behaviors considering time as an elemental variable. We propose an approach which starts from a Petri net process model constructed by a process mining algorithm. We enhance the Petri net model with time decay functions to create continuous process state samples. Finally, we use these samples in combination with discrete token movement counters and Petri net markings to train a deep learning model that predicts the next event. We demonstrate significant performance improvements and outperform the state-of-the-art methods on nine real-world benchmark event logs. [Description provided by NIOSH]
• Subjects:
  Behavior Occupational Health Safety Software
• Keywords:
  Author Keywords: Business Process Intelligence Computer Applications Computerized Simulation Computer Software Decay Functions Deep Learning Human Factors Engineering Models Neural Networks Operational Runtime Support Petri Nets Predictive Process Management Process Mining

  More +
• ISSN:
  2169-3536
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Illinois ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  7
• NIOSHTIC Number:
  nn:20063652
• Citation:
  IEEE Access 2019 Aug; 7:119787-119803
• Contact Point Address:
  Houshang Darabi, Mechanical and Industrial Engineering Department, University of Illinois at Chicago, Chicago, IL 60607, USA
• Email:
  hdarabi@uic.edu
• Federal Fiscal Year:
  2019
• Performing Organization:
  University of Illinois at Chicago
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  IEEE Access
• End Date:
  20290630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f28466819f6e6b75027204a309765eb9eb6d5954ef058533aa5dcfbca21e4dd3f34cf4bf63c00fee534d4fccaf8beff2aafadcc57801844d883a266e2922b402
• Download URL:
  https://stacks.cdc.gov/view/cdc/222220/cdc_222220_DS1.pdf
• File Type:
  [PDF - 7.15 MB ]