Data-Driven Performance Analysis of Scheduled Processes

Arik Senderovich, Andreas Rogge-Solti, Avigdor Gal, Jan Mendling, Avishai Mandelbaum, Sarah Kadish, Craig A. Bunnell

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.

Original languageEnglish
Title of host publicationBUSINESS PROCESS MANAGEMENT, BPM 2015
EditorsJan Recker, Matthias Weidlich, Hamid Reza Motahari-Nezhad
Number of pages18
StatePublished - 2015
Event13th International Conference on Business Process Management, BPM 2015 - Innsbruck, Austria
Duration: 31 Aug 20153 Sep 2015

Publication series

NameLecture Notes in Computer Science


Conference13th International Conference on Business Process Management, BPM 2015

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Data-Driven Performance Analysis of Scheduled Processes'. Together they form a unique fingerprint.

Cite this