Abstract
Linked Data (LD) technology enables integrating information across disparate sources and can be exploited to perform inferencing for deriving added-value knowledge. As such, it can really support performing different kinds of analysis tasks over business process (BP) execution related information. When moving BPs in the cloud, giving rise to Business Process as a Service (BPaaS) concept, the first main challenge is to collect and link, based on a certain structure, information originating from different systems. To this end, two main ontologies are proposed in this paper to enable this structuring: a KPI and a Dependency one. Then, via exploiting these well-connected ontologies, an innovative Key Performance Indicator (KPI) analysis system is built that offers two main analysis capabilities: KPI assessment and drill-down, where the second can enable finding root causes of KPI violations. This system advances the state-of-the-art by exhibiting the capability, through the LD usage, of the flexible construction and assessment of any KPI kind, allowing experts to better explore the possible KPI space.
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References
Karagiannis, D.: BPMS: Business Process Management Systems. SIGOIS Bull. 16, 10–13 (1995)
Caplan, R.S., Norton, D.P.: The balanced scorecard measures that drive performance. Harvard Bus. Rev. 70, 281–308 (1992)
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., Zeng, L.: Model driven development for business performance management. IBM Syst. J. 45, 587–605 (2006)
Castellanos, M., Casati, F., Shan, M.C., Dayal, U.: IBOM: a platform for intelligent business operation management. In: ICDE, pp. 1084–1095. IEEE Computer Society, Washington, DC (2005)
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., Plexousakis, D.: D4.1 - First CloudSocket Architecture. CloudSocket European Project (2015)
Kritikos, K., Plexousakis, D.: Semantic QoS metric matching. In: ECOWS, pp. 265–274. IEEE Computer Society (2006)
Kritikos, K., Plexousakis, D., Woitsch, R.: Towards semantic KPI measurement. In: CLOSER, pp. 63–74. SciTePress, Porto (2017)
List, B., Korherr, B.: An evaluation of conceptual business process modelling languages. In: SAC, pp. 1532–1539. ACM, Dijon (2006)
Wetzstein, B., Karastoyanova, D., Leymann, F.: Towards management of SLA-aware business processes based on key performance indicators. In: BPMDS, Montpellier, France (2008)
Motta, G., Pignatelli, G., Florio, M.: Performing business process knowledge base. In: First International Workshop and Summer School on Service Science, Heraklion, Greece (2007)
Pierantonio, A., Rosa, G., Silingas, D., Thönssen, B., Woitsch, R.: Metamodeling architectures for business processes in organizations. In: Proceedings of the Projects Showcase at STAF, L’Aquila, Italy. CEUR (2015)
Friedenstab, J.P., Janiesch, C., Matzner, M., Muller, O.: Extending BPMN for business activity monitoring. In: HICSS, pp. 4158–4167. IEEE Computer Society (2012)
Frank, U., Heise, D., Kattenstroth, H., Schauer, H.: Designing and utilising business indicator systems within enterprise models: outline of a method. In: MobIS: Modellierung zwischen SOA und Compliance Management, Saarbröcken, Germany (2008)
González, O., Casallas, R., Deridder, D.: MMC-BPM: a domain-specific language for business processes analysis. In: Abramowicz, W. (ed.) BIS 2009. LNBIP, vol. 21, pp. 157–168. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01190-0_14
del Río-Ortega, A., Resinas, M., Durán, A., Ruiz-Cortés, A.: Using templates and linguistic patterns to define process performance indicators. Enterp. Inf. Syst. 10, 159–192 (2016)
Costello, C., Malloy, O.: Building a process performance model for business activity monitoring. In: Wojtkowski, W., Wojtkowski, G., Lang, M., Conboy, K., Barry, C. (eds.) Information Systems Development - Challenges in Practice, Theory, and Education, pp. 237–248. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-68772-8_19
Liu, R., Nigam, A., Jeng, J., Shieh, C., Wu, F.Y.: Integrated modeling of performance monitoring with business artifacts. In: ICEBE, pp. 64–71. IEEE Computer Society, Shanghai (2010)
Seedorf, S., Schader, M.: Towards an enterprise software component ontology. In: AMCIS. Association for Information Systems (2011)
Gruschke, B.: Integrated event management: event correlation using dependency graphs. In: DSOM (1998)
Cui, Y., Nahrstedt, K.: QoS-aware dependency management for component-based systems. In: HPDC, p. 127. IEEE Computer Society (2001)
Hasselmeyer, P.: Managing dynamic service dependencies. In: DSOM, pp. 141–150. Inria, Nancy (2001)
Rossini, A., Kritikos, K., Nikolov, N., Domaschka, J., Griesinger, F., Seybold, D., Romero, D.: D2.1.3 - CloudML Implementation Documentation (Final version). Paasage project deliverable (2015)
Wetzstein, B., Leitner, P., Rosenberg, F., Brandic, I., Dustdar, S., Leymann, F.: Monitoring and analyzing influential factors of business process performance. In: EDOC, pp. 118–127. IEEE Press (2009)
Wetzstein, B., Ma, Z., Leymann, F.: Towards measuring key performance indicators of semantic business processes. In: Abramowicz, W., Fensel, D. (eds.) BIS 2008. LNBIP, vol. 7, pp. 227–238. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79396-0_20
Diamantini, C., Potena, D., Storti, E., Zhang, H.: An ontology-based data exploration tool for key performance indicators. In: Meersman, R., et al. (eds.) OTM 2014. LNCS, vol. 8841, pp. 727–744. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45563-0_45
Kritikos, K., Pernici, B., Plebani, P., Cappiello, C., Comuzzi, M., Benbernou, S., Brandic, I., Kertész, A., Parkin, M., Carro, M.: A survey on service quality description. ACM Comput. Surv. 46, 1 (2013)
de Medeiros, A.K.A., et al.: An outlook on semantic business process mining and monitoring. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2007. LNCS, vol. 4806, pp. 1244–1255. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76890-6_52
Kritikos, K., Magoutis, K., Plexousakis, D.: Towards knowledge-based assisted IaaS selection. In: CloudCom. IEEE Computer Society, Luxembourg (2016)
Zeginis, C., Kritikos, K., Plexousakis, D.: Event pattern discovery in multi-cloud service-based applications. Int. J. Syst. Serv. Oriented Eng. 5, 78–103 (2015)
Kritikos, K., Plexousakis, D.: Semantic SLAs for services with Q-SLA. In: ICWS, pp. 686–689. IEEE Computer Society, San Francisco (2016)
Kritikos, K., Zegkinis, C., Seybold, D., Griesinger, F.: D3.6 - BPaaS Monitoring and Evaluation Prototypes. CloudSocket European Project (2017)
Acknowledgements
This research has received funding from the European Community’s Framework Programme for Research and Innovation HORIZON 2020 (ICT-07-2014) under grant agreement number 644690 (CloudSocket).
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Kritikos, K., Plexousakis, D., Woitch, R. (2018). A Flexible Semantic KPI Measurement System. In: Ferguson, D., Muñoz, V., Cardoso, J., Helfert, M., Pahl, C. (eds) Cloud Computing and Service Science. CLOSER 2017. Communications in Computer and Information Science, vol 864. Springer, Cham. https://doi.org/10.1007/978-3-319-94959-8_13
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