Abstract
The dynamic nature of the Web and its increasing importance as an economic platform create the need of new methods and tools for business efficiency. Current Web analytic tools do not provide the necessary abstracted view of the underlying customer processes and critical paths of site visitor behavior. Such information can offer insights for businesses to react effectively and efficiently. We propose applying Business Process Management (BPM) methodologies to e-commerce Website logs, and present the challenges, results and potential benefits of such an approach.
We use the Business Process Insight (BPI) platform, a collaborative process intelligence toolset that implements the discovery of loosely-coupled processes, and includes novel process mining techniques suitable for the Web. Experiments are performed on custom click-stream logs from a large online travel and booking agency. We first compare Web clicks and BPM events, and then present a methodology to classify and transform URLs into events. We evaluate traditional and custom process mining algorithms to extract business models from real-life Web data. The resulting models present an abstracted view of the relation between pages, exit points, and critical paths taken by customers. Such models show important improvements and aid high-level decision making and optimization of e-commerce sites compared to current state-of-art Web analytics.
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References
Aalst, W., et al.: Process mining manifesto. In: Business Process Management Workshops, vol. 99, Springer, Heidelberg (2012)
Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)
Bhushan, R., Nath, R.: Automatic recommendation of web pages for online users using web usage mining. In: ICCS (2012)
De Weerdt, J., et al.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7) (2012)
Ferreira, D.R., Gillblad, D.: Discovering process models from unlabelled event logs. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 143–158. Springer, Heidelberg (2009)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explorations 11(1) (2009)
Kemsley, S.: It’s not about BPM vs. ACM, it’s about a spectrum of process functionality, http://www.column2.com/2011/03/its-not-about-bpm-vs-acm-its-about-a-spectrum-of-process-functionality/
Koehler, J.: Business process modeling
Kumar, L., Singh, H., Kaur, R.: Web analytics and metrics: a survey. In: ACM ICACCI (2012)
Menascé, D.A., Almeida, V.A., Fonseca, R., Mendes, M.A.: A methodology for workload characterization of e-commerce sites. In: ACM EC (1999)
Nezhad, H.R.M., Saint-Paul, R., Casati, F., Benatallah, B.: Event correlation for process discovery from web service interaction logs. VLDB J. 20(3) (2011)
Nielsen. Trends in online shopping, a Nielsen Consumer report. Technical report, Nielsen (February 2008)
Pfeffer, A.: Functional specification of probabilistic process models. In: AAAI (2005)
Poggi, N., Carrera, D., Gavald, R., Ayguad, E., Torres, J.: A methodology for the evaluation of high response time on e-commerce users and sales. In: ISF (2012)
Poggi, N., et al.: Characterization of workload and resource consumption for an online travel and booking site. In: IEEE IISWC (2010)
Rembert, A.J., Ellis, C.S.: Learning the control-flow of a business process using icn-based process models. In: ACM ICSOC, pp. 346–351 (2009)
Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering colored petri nets from event logs. STTTÂ 10(1) (2008)
Rozinat, A., van der Aalst, W.M.P.: Decision mining in ProM. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420–425. Springer, Heidelberg (2006)
Rozsnyai, S., et al.: Business process insight: An approach and platform for the discovery and analysis of end-to-end business processes. In: IEEE SRII (2012)
Rozsnyai, S., Slominski, A., Lakshmanan, G.T.: Discovering event correlation rules for semi-structured business processes. In: ACM DEBS (2011)
Sharma, K., Shrivastava, G., Kumar, V.: Web mining: Today and tomorrow. In: ICECT, vol. 1 (2011)
Spiliopoulou, M., Pohle, C., Faulstich, L.C.: Improving the effectiveness of a web site with web usage mining. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 142–162. Springer, Heidelberg (2000)
van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer (2011)
van der Aalst, W.M.P.: et al. Workflow mining: a survey of issues and approaches. Data Knowl. Eng., 47(2) (November 2003)
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
van der Aalst, W.M.P., van Dongen, B.F., Gunther, C.W., Rozinat, A., Verbeek, E., Weijters, T.: ProM: The process mining toolkit. In: BPM (Demos) (2009)
Waisberg, D., et al.: Web analytics 2.0: Empowering customer centricity (2009)
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Poggi, N., Muthusamy, V., Carrera, D., Khalaf, R. (2013). Business Process Mining from E-Commerce Web Logs. In: Daniel, F., Wang, J., Weber, B. (eds) Business Process Management. Lecture Notes in Computer Science, vol 8094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40176-3_7
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DOI: https://doi.org/10.1007/978-3-642-40176-3_7
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