Narrowing the business-IT gap in process performance measurement

Han van der Aa*, Adela Del-Río-Ortega, Manuel Resinas, Henrik Leopold, Antonio Ruiz-Cortés, Jan Mendling, Hajo A. Reijers

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

To determine whether strategic goals are met, organizations must monitor how their business processes perform. Process Performance Indicators (PPIs) are used to specify relevant performance requirements. The formulation of PPIs is typically a managerial concern. Therefore, considerable effort has to be invested to relate PPIs, described by management, to the exact operational and technical characteristics of business processes. This work presents an approach to support this task, which would otherwise be a laborious and time-consuming endeavor. The presented approach can automatically establish links between PPIs, as formulated in natural language, with operational details, as described in process models. To do so, we employ machine learning and natural language processing techniques. A quantitative evaluation on the basis of a collection of 173 real-world PPIs demonstrates that the proposed approach works well.

Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings
PublisherSpringer - Verlag
Pages543-557
Number of pages15
Volume9694
ISBN (Print)9783319396958
DOIs
Publication statusPublished - 2016
Event28th International Conference on Advanced Information Systems Engineering, CAiSE 2016 - Ljubljana, Slovenia
Duration: 13 Jun 201617 Jun 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9694
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference28th International Conference on Advanced Information Systems Engineering, CAiSE 2016
CountrySlovenia
CityLjubljana
Period13/06/1617/06/16

Keywords

  • Model alignment
  • Natural language processing
  • Performance measurement
  • Process performance indicators

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