Abstract
Process Mining aims to discover and evaluate As-Is processes from sets of sequential events, by examining different instances of the same process and building models that can detect patterns and behaviors. In the meanwhile, organizational perspective is being considered in Process Mining by taking advantage of the ability to extract social networks that represent different kinds of relations between resources performing the process. The case study tries to describe how Process Mining could be applied in order to detect and improve “Customer Relationship Management” process and extract some kind of social networks that represent the relations between the employees(resources) of National Institute of Statistics of Portugal (INE) using event logs.
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References
Van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)
ProM: Prom 6 tutorial. http://www.promtools.org/prom6/downloads/prom-6.0-tutorial.pdf (2010)
Van der Aalst, W., Song, M.: Mining social networks: uncovering interaction patterns in business processes. In: International Conference on Business Process Management. Springer, Berlin (2004)
Mans, R.S., Schonenberg, M.H., Song, M., et al.: Application of process mining in healthcare : a case study in a Dutch hospital. Biomed. Eng. Syst. Technol. 25, 425–438 (2009)
van der Aalst, W.M.P., Reijers, H.A., Weijters, A., et al.: Business process mining: an industrial application. Inf. Syst. 32(5), 713–732 (2007)
INE: Statistics Portugal. https://www.ine.pt/xportal/xmain?xpgid=ine_main&xpid=INE (2015)
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: OTM 2012: On the Move to Meaningful Internet Systems: OTM 2012, pp. 305–322. Springer, Berlin (2012)
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (fhm). In: IEEE Symposium on Computational Intelligence and Data 565 Mining (CIDM), pp. 310–317 (2011)
Van Eck, M. L., Buijs, J.C.A.M., van Dongen, B.F.: Genetic process mining: alignment-based process model mutation. In: Business Process Management Workshops, pp. 291–303. Springer International Publishing, Cham (2015)
CROSS, R.: Knowing what we know: supporting knowledge creation and sharing in social networks. Organ. Dyn. 30, 100120 (2001)
Song, M., van der Aalst, W.: Towards comprehensive support for organizational mining. Decis. Support. Syst. 46(1), 300317 (2008)
Acknowledgements
This work was supported by the research project TEC4Growth—Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020, , North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF) and the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization—COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Fundao para a Cincia e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.
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Fares, A., Gama, J., Campos, P. (2019). Process Mining for Analyzing Customer Relationship Management Systems: A Case Study. In: Sayed-Mouchaweh, M. (eds) Learning from Data Streams in Evolving Environments. Studies in Big Data, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-89803-2_9
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DOI: https://doi.org/10.1007/978-3-319-89803-2_9
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