Improving process models by discovering decision points

Sharmila Subramaniam, Vana Kalogeraki, Dimitrios Gunopulos, Fabio Casati, Malu Castellanos, Umeshwar Dayal, Mehmet Sayal

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Workflow management systems (WfMS) are widely used by business enterprises as tools for administrating, automating and scheduling the business process activities with the available resources. Since the control flow specifications of workflows are manually designed, they entail assumptions and errors, leading to inaccurate workflow models. Decision points, the XOR nodes in a workflow graph model, determine the path chosen toward completion of any process invocation. In this work, we show that positioning the decision points at their earliest points can improve process efficiency by decreasing their uncertainties and identifying redundant activities. We present novel techniques to discover the earliest positions by analyzing workflow logs and to transform the model graph. The experimental results show that the transformed model is more efficient with respect to its average execution time and uncertainty, when compared to the original model.

Original languageEnglish
Pages (from-to)1037-1055
Number of pages19
JournalInformation Systems
Volume32
Issue number7
DOIs
Publication statusPublished - Nov 2007
Externally publishedYes

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Keywords

  • Classification
  • Process mining
  • Workflow graph models

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture

Cite this

Subramaniam, S., Kalogeraki, V., Gunopulos, D., Casati, F., Castellanos, M., Dayal, U., & Sayal, M. (2007). Improving process models by discovering decision points. Information Systems, 32(7), 1037-1055. https://doi.org/10.1016/j.is.2006.11.001