Towards Semantic KPI Measurement

Kyriakos Kritikos, Dimitris Plexousakis, Robert Woitsch


Linked Data (LD) represent a great mechanism towards integrating information across disparate sources. The respective technology can also be exploited to perform inferencing for deriving added-value knowledge. As such, LD technology can really assist in performing various analysis tasks over information related to business process execution. In the context of Business Process as a Service (BPaaS), the first real challenge is to collect and link information originating from different systems by following a certain structure. As such, this paper proposes two main ontologies that serve this purpose: a KPI and a Dependency one. Based on these well-connected ontologies, an innovative Key Performance Indicator (KPI) analysis system is then built which exhibits two main analysis capabilities: KPI assessment and drill-down, where the second can be exploited to find root causes of KPI violations. Compared to other KPI analysis systems, LD usage enables the flexible construction and assessment of any KPI kind allowing experts to better explore the possible KPI space.


  1. Caplan, R. S. and Norton, D. P. (1992). The Balanced Scorecard Measures that Drive Performance. Harvard Business Review, 70(1):281-308.
  2. Castellanos, M., Casati, F., Shan, M.-C., and Dayal, U. (2005). ibom: A platform for intelligent business operation management. In ICDE, pages 1084-1095, Washington, DC, USA. IEEE Computer Society.
  3. Chowdhary, P., Bhaskaran, K., Caswell, N. S., Chang, H., Chao, T., Chen, S.-K., Dikun, M., Lei, H., Jeng, J.- J., Kapoor, S., Lang, C. A., Mihaila, G., Stanoi, I., and Zeng, L. (2006). Model Driven Development for Business Performance Management. IBM Syst. J., 45(3):587-605.
  4. Costello, C. and Malloy, O. (2008). Building a Process Performance Model for Business Activity Monitoring. In Wojtkowski, W., Wojtkowski, G., Lang, M., Conboy, K., and Barry, C., editors, Information Systems Development - Challenges in Practice, Theory, and Education, pages 237-248. Springer-Verlag.
  5. Cui, Y. and Nahrstedt, K. (2001). QoS-Aware Dependency Management for Component-Based Systems. In HPDC, page 127. IEEE Computer Society.
  6. De Medeiros, A. K. A., Pedrinaci, C., van der Aalst, W. M. P., Domingue, J., Song, M., Rozinat, A., Norton, B., and Cabral, L. (2007). An Outlook on Semantic Business Process Mining and Monitoring. In OTM, pages 1244-1255. Springer-Verlag.
  7. del Río-Ortega, A., Resinas, M., Dur án, A., and RuizCortés, A. (2016). Using templates and linguistic patterns to define process performance indicators. Enterp. Inf. Syst., 10(2):159-192.
  8. Diamantini, C., Potena, D., Storti, E., and Zhang, H. (2014). An Ontology-Based Data Exploration Tool for Key Performance Indicators. In ODBASE, pages 727-744, Amantea,Italy. Springer-Verlag.
  9. Frank, U., Heise, D., Kattenstroth, H., and Schauer, H. (2008). Designing and utilising business indicator systems within enterprise models: Outline of a method. In MobIS: Modellierung zwischen SOA und Compliance Management, Saarbröcken, Germany.
  10. Friedenstab, J.-P., Janiesch, C., Matzner, M., and Muller, O. (2012). Extending BPMN for Business Activity Monitoring. In HICSS, pages 4158-4167. IEEE Computer Society.
  11. González, O., Casallas, R., and Deridder, D. (2009). MMCBPM: A domain-specific language for business processes analysis. In BIS, volume 21, pages 157-168, Poznan, Poland. Springer.
  12. Gruschke, B. (1998). Integrated Event Management: Event Correlation Using Dependency Graphs. In DSOM.
  13. Hasselmeyer, P. (2001). Managing Dynamic Service Dependencies. In DSOM, pages 141-150, Nancy, France. INRIA.
  14. Kritikos, K., Magoutis, K., and Plexousakis, D. (2016). Towards Knowledge-Based Assisted IaaS Selection. In CloudCom, Luxembourg. IEEE Computer Society.
  15. Kritikos, K., Pernici, B., Plebani, P., Cappiello, C., Comuzzi, M., Benbernou, S., Brandic, I., Kertész, A., Parkin, M., and Carro, M. (2013). A survey on service quality description. ACM Comput. Surv., 46(1):1.
  16. Kritikos, K. and Plexousakis, D. (2006). Semantic QoS Metric Matching. In ECOWS, pages 265-274. IEEE Computer Society.
  17. Liu, R., Nigam, A., Jeng, J., Shieh, C., and Wu, F. Y. (2010). Integrated Modeling of Performance Monitoring with Business Artifacts. In ICEBE, pages 64-71, Shanghai, China. IEEE Computer Society.
  18. Motta, G., Pignatelli, G., , and Florio, M. (2007). Performing Business Process Knowledge Base. In First Internation Workshop and Summer School on Service Science, Heraklion, Greece.
  19. Pierantonio, A., Rosa, G., Silingas, D., Thönssen, B., and Woitsch, R. (2015). Metamodeling Architectures for Business Processes in Organizations. In Proceedings of the Projects Showcase at STAF, L'Aquila, Italy. CEUR.
  20. Rossini, A., Kritikos, K., Nikolov, N., Domaschka, J., Griesinger, F., Seybold, D., and Romero, D. (2015). D2.1.3 - CloudML Implementation Documentation (Final version). Paasage project deliverable.
  21. Seedorf, S. and Schader, M. (2011). Towards an Enterprise Software Component Ontology. In AMCIS. Association for Information Systems.
  22. Wetzstein, B., Karastoyanova, D., and Leymann, F. (2008a). Towards Management of SLA-Aware Business Processes Based on Key Performance Indicators. In BPMDS, Montpellier, France.
  23. Wetzstein, B., Leitner, P., Rosenberg, F., Brandic, I., Dustdar, S., and Leymann, F. (2009). Monitoring and Analyzing Influential Factors of Business Process Performance. In EDOC, pages 118-127. IEEE Press.
  24. Wetzstein, B., Ma, Z., and Leymann, F. (2008b). Towards Measuring Key Performance Indicators of Semantic Business Processes. In BIS, page 227238. SpringerVerlag.
  25. Woitsch, R., Albayrak, M., Köhn, H., Utz, W., Ferrer, A. J., Iranzo, J., Leonforte, A., Gallo, A., Mihnea, V., Pacurar, R., Avasilcai, C., Arama, G., Boca, R., Griesinger, F., Seybold, D., Domaschka, J., Kritikos, K., and Plexousakis, D. (2015). D4.1 - First CloudSocket Architecture. CloudSocket European Project.

Paper Citation

in Harvard Style

Kritikos K., Plexousakis D. and Woitsch R. (2017). Towards Semantic KPI Measurement . In Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-243-1, pages 91-102. DOI: 10.5220/0006238000910102

in Bibtex Style

author={Kyriakos Kritikos and Dimitris Plexousakis and Robert Woitsch},
title={Towards Semantic KPI Measurement},
booktitle={Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},

in EndNote Style

JO - Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Towards Semantic KPI Measurement
SN - 978-989-758-243-1
AU - Kritikos K.
AU - Plexousakis D.
AU - Woitsch R.
PY - 2017
SP - 91
EP - 102
DO - 10.5220/0006238000910102