Narrowing the business-IT gap in process performance measurement

H. Van Der Aa; A. Del-Río-Ortega; M. Resinas; H. Leopold; A. Ruiz-Cortés; J. Mendling; H.A. Reijers

doi:10.1007/978-3-319-39696-5_33

Narrowing the business-IT gap in process performance measurement

H. Van Der Aa, A. Del-Río-Ortega, M. Resinas, H. Leopold, A. Ruiz-Cortés, J. Mendling, H.A. Reijers

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

7 Citations (Scopus)

1 Downloads (Pure)

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
Subtitle of host publication	28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings
Editors	S. Nurcan, P. Soffer, M. Bajec, J. Eder
Place of Publication	Dordrecht
Publisher	Springer
Pages	543-557
Number of pages	15
ISBN (Electronic)	978-3-319-39696-5
ISBN (Print)	978-3-319-39695-8
DOIs	https://doi.org/10.1007/978-3-319-39696-5_33
Publication status	Published - 2016
Event	28th International Conference on Advanced Information Systems Engineering (CAiSE 2016) - Ljubljana, Slovenia Duration: 13 Jun 2016 → 17 Jun 2016 Conference number: 28 http://caise2016.si/

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)
Abbreviated title	CAiSE '16
Country	Slovenia
City	Ljubljana
Period	13/06/16 → 17/06/16
Other	"Information Systems for Connecting People"
Internet address	http://caise2016.si/

Keywords

Model alignment
Natural language processing
Performance measurement
Process performance indicators

Access to Document

10.1007/978-3-319-39696-5_33

Cite this

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 S. Nurcan, P. Soffer, M. Bajec, & J. Eder (Eds.), Advanced Information Systems Engineering : 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings (pp. 543-557). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9694). Springer. https://doi.org/10.1007/978-3-319-39696-5_33

Van Der Aa, H. ; Del-Río-Ortega, A. ; Resinas, M. ; Leopold, H. ; Ruiz-Cortés, A. ; Mendling, J. ; Reijers, H.A. / Narrowing the business-IT gap in process performance measurement. Advanced Information Systems Engineering : 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. editor / S. Nurcan ; P. Soffer ; M. Bajec ; J. Eder. Dordrecht : Springer, 2016. pp. 543-557 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3015afa96c6040c9a56ff87165490afe,

title = "Narrowing the business-IT gap in process performance measurement",

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.",

keywords = "Model alignment, Natural language processing, Performance measurement, Process performance indicators",

author = "{Van Der Aa}, H. and A. Del-R{\'i}o-Ortega and M. Resinas and H. Leopold and A. Ruiz-Cort{\'e}s and J. Mendling and H.A. Reijers",

year = "2016",

doi = "10.1007/978-3-319-39696-5_33",

language = "English",

isbn = "978-3-319-39695-8",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "543--557",

editor = "Nurcan, {S. } and Soffer, {P. } and M. Bajec and J. Eder",

booktitle = "Advanced Information Systems Engineering",

address = "Germany",

note = "28th International Conference on Advanced Information Systems Engineering (CAiSE 2016), CAiSE '16 ; Conference date: 13-06-2016 Through 17-06-2016",

url = "http://caise2016.si/",

}

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 S Nurcan, P Soffer, M Bajec & J Eder (eds), Advanced Information Systems Engineering : 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9694, Springer, Dordrecht, pp. 543-557, 28th International Conference on Advanced Information Systems Engineering (CAiSE 2016), Ljubljana, Slovenia, 13/06/16. https://doi.org/10.1007/978-3-319-39696-5_33

Narrowing the business-IT gap in process performance measurement. / Van Der Aa, H.; Del-Río-Ortega, A.; Resinas, M.; Leopold, H.; Ruiz-Cortés, A.; Mendling, J.; Reijers, H.A.

Advanced Information Systems Engineering : 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. ed. / S. Nurcan; P. Soffer; M. Bajec; J. Eder. Dordrecht : Springer, 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

TY - GEN

T1 - Narrowing the business-IT gap in process performance measurement

AU - Van Der Aa, H.

AU - Del-Río-Ortega, A.

AU - Resinas, M.

AU - Leopold, H.

AU - Ruiz-Cortés, A.

AU - Mendling, J.

AU - Reijers, H.A.

N1 - Conference code: 28

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Model alignment

KW - Natural language processing

KW - Performance measurement

KW - Process performance indicators

UR - http://www.scopus.com/inward/record.url?scp=84976640067&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-39696-5_33

DO - 10.1007/978-3-319-39696-5_33

M3 - Conference contribution

AN - SCOPUS:84976640067

SN - 978-3-319-39695-8

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 543

EP - 557

BT - Advanced Information Systems Engineering

A2 - Nurcan, S.

A2 - Soffer, P.

A2 - Bajec, M.

A2 - Eder, J.

PB - Springer

CY - Dordrecht

T2 - 28th International Conference on Advanced Information Systems Engineering (CAiSE 2016)

Y2 - 13 June 2016 through 17 June 2016

ER -

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 Nurcan S, Soffer P, Bajec M, Eder J, editors, Advanced Information Systems Engineering : 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. Dordrecht: Springer. 2016. p. 543-557. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-39696-5_33

Narrowing the business-IT gap in process performance measurement

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this