A Hybrid Model for Business Process Event Prediction
Process event prediction is the prediction of various properties of the remaining path of a process sequence or workflow. The prediction is based on the data extracted from a combination of historical (closed) and/or live (open) workflows (jobs or process instances). In real-world applications, the problem is compounded by the fact that the number of unique workflows (process prototypes) can be enormous, their occurrences can be limited, and a real process may deviate from the designed process when executed in real environment and under realistic constraints. It is necessary for an efficient predictor to be able to cope with the diverse characteristics of the data.We also have to ensure that useful process data is collected to build the appropriate predictive model. In this paper we propose an extension of Markov models for predicting the next step in a process instance.We have shown, via a set of experiments, that our model offers better results when compared to methods based on random guess, Markov models and Hidden Markov models. The data for our experiments comes from a real live process in a major telecommunication company.
KeywordsMarkov Model Hide Markov Model Transition Matrix Hide State Process Instance
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- 2.MJA Berry and GS Linoff, Data mining techniques: for marketing, sales, and customer relationship management, Wiley New York, 2004.Google Scholar
- 4.M Deshpande and G Karypis, ‘Selective markov models for predicting web page accesses’, ACM Transactions on Internet Technology (TOIT), 4(2), 163–184, (2004).Google Scholar
- 5.M Eirinaki, M Vazirgiannis, and D Kapogiannis, ‘Web path recommendations based on page ranking and markov models’, in Proceedings of the 7th annual ACM international workshop on Web information and data management, WIDM ’05, pp. 2–9, (2005).Google Scholar
- 6.O Netzer, J Lattin, and VS Srinivasan, ‘A hidden markov model of customer relationship dynamics’, (2007).Google Scholar
- 7.A Papoulis, Probability, Random Variables and Stochastic Processes, 1991.Google Scholar
- 8.J Pitkow and P Pirolli, ‘Mining longest repeating subsequences to predict world wide web surfing’, in The 2nd USENIX Symposium on Internet Technologies & Systems, pp. 139–150, (1999).Google Scholar
- 9.D Ruta and B Majeed, ‘Business process forecasting in telecommunication industry’, 1–5, (2010).Google Scholar
- 10.P Taylor, M Leida, and BA Majeed, ‘Case study in process mining in a multinational enterprise’, in Lecture Notes in Business Information Processing. Springer, (2012).Google Scholar
- 11.S Thede and M Happer, ‘A second-order hidden markov model for part-of-speech tagging’, 175–182, (1999).Google Scholar
- 12.W van der Aalst, Process mining: Discovery, conformance and enhancement of business processes,Springer-Verlag, Berlin, 2011.Google Scholar
- 13.M Waterman, ‘Estimating statistical significance of sequence alignments’, Philosophical transactions-Royal society of London series B biological sciences, 344, 383–390, (1994).Google Scholar