Data-Driven Performance Analysis of Scheduled Processes

  • Arik Senderovich
  • Andreas Rogge-Solti
  • Avigdor Gal
  • Jan Mendling
  • Avishai Mandelbaum
  • Sarah Kadish
  • Craig A. Bunnell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9253)

Abstract

The performance of scheduled business processes is of central importance for services and manufacturing systems. However, current techniques for performance analysis do not take both queueing semantics and the process perspective into account. In this work, we address this gap by developing a novel method for utilizing rich process logs to analyze performance of scheduled processes. The proposed method combines simulation, queueing analytics, and statistical methods. At the heart of our approach is the discovery of an individual-case model from data, based on an extension of the Colored Petri Nets formalism. The resulting model can be simulated to answer performance queries, yet it is computational inefficient. To reduce the computational cost, the discovered model is projected into Queueing Networks, a formalism that enables efficient performance analytics. The projection is facilitated by a sequence of folding operations that alter the structure and dynamics of the Petri Net model. We evaluate the approach with a real-world dataset from Dana-Farber Cancer Institute, a large outpatient cancer hospital in the United States.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arik Senderovich
    • 1
  • Andreas Rogge-Solti
    • 2
  • Avigdor Gal
    • 1
  • Jan Mendling
    • 2
  • Avishai Mandelbaum
    • 1
  • Sarah Kadish
    • 3
  • Craig A. Bunnell
    • 3
  1. 1.Technion – Israel Institute of TechnologyHaifaIsrael
  2. 2.Vienna University of Economics and BusinessWienAustria
  3. 3.Dana-Farber Cancer InstituteBostonUSA

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