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

doi:10.1007/978-3-319-39696-533

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 proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Advanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings
Publisher	Springer - Verlag
Pages	543-557
Number of pages	15
Volume	9694
ISBN (Print)	9783319396958
DOIs	https://doi.org/10.1007/978-3-319-39696-533
Publication status	Published - 2016
Event	28th International Conference on Advanced Information Systems Engineering, CAiSE 2016 - Ljubljana, Slovenia Duration: 13 Jun 2016 → 17 Jun 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9694
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	28th International Conference on Advanced Information Systems Engineering, CAiSE 2016
Country	Slovenia
City	Ljubljana
Period	13/06/16 → 17/06/16

Keywords

Model alignment
Natural language processing
Performance measurement
Process performance indicators

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van der Aa, H., Del-Río-Ortega, A., Resinas, M., Leopold, H., Ruiz-Cortés, A., Mendling, J., & Reijers, H. A. (2016). Narrowing the business-IT gap in process performance measurement. In Advanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings (Vol. 9694, pp. 543-557). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9694). Springer - Verlag. https://doi.org/10.1007/978-3-319-39696-533

van der Aa, Han ; Del-Río-Ortega, Adela ; Resinas, Manuel ; Leopold, Henrik ; Ruiz-Cortés, Antonio ; Mendling, Jan ; Reijers, Hajo A. / Narrowing the business-IT gap in process performance measurement. Advanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings. Vol. 9694 Springer - Verlag, 2016. pp. 543-557 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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van der Aa, H, Del-Río-Ortega, A, Resinas, M, Leopold, H, Ruiz-Cortés, A, Mendling, J & Reijers, HA 2016, Narrowing the business-IT gap in process performance measurement. in Advanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings. vol. 9694, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9694, Springer - Verlag, pp. 543-557, 28th International Conference on Advanced Information Systems Engineering, CAiSE 2016, Ljubljana, Slovenia, 13/06/16. https://doi.org/10.1007/978-3-319-39696-533

Narrowing the business-IT gap in process performance measurement. / van der Aa, Han; Del-Río-Ortega, Adela; Resinas, Manuel; Leopold, Henrik; Ruiz-Cortés, Antonio; Mendling, Jan; Reijers, Hajo A.

Advanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings. Vol. 9694 Springer - Verlag, 2016. p. 543-557 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9694).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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van der Aa H, Del-Río-Ortega A, Resinas M, Leopold H, Ruiz-Cortés A, Mendling J et al. Narrowing the business-IT gap in process performance measurement. In Advanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings. Vol. 9694. Springer - Verlag. 2016. p. 543-557. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-39696-533