Abstract
Anticipating the next events of an ongoing series of activities has many compelling applications in various industries. It can be used to improve customer satisfaction, to enhance operational efficiency, and to streamline health-care services, to name a few. In this work, we propose an algorithm that predicts the next events by leveraging business process models obtained using process mining techniques. Because we are using business process models to build the predictions, it allows business analysts to interpret and alter the predictions. We tested our approach with more than 30 synthetic datasets as well as 6 real datasets. The results have superior accuracy compared to using neural networks while being orders of magnitude faster.
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Notes
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available here: https://verenich.github.io/ProcessSequencePrediction/.
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https://data.4tu.nl/repository/uuid:745584e7-8cc0-45b8-8a89-93e9c9dfab05, sets ‘1 - scalability’, ‘round 3 to 5’.
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Bernard, G., Andritsos, P. (2019). Accurate and Transparent Path Prediction Using Process Mining. In: Welzer, T., Eder, J., Podgorelec, V., Kamišalić Latifić, A. (eds) Advances in Databases and Information Systems. ADBIS 2019. Lecture Notes in Computer Science(), vol 11695. Springer, Cham. https://doi.org/10.1007/978-3-030-28730-6_15
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