Activity prediction in process mining using the WoMan framework

Abstract

Process Management techniques are useful in domains where the availability of a (formal) process model can be leveraged to monitor, supervise, and control a production process. While their classical application is in the business and industrial fields, other domains may profitably exploit Process Management techniques. Some of these domains (e.g., people’s behavior, General Game Playing) are much more flexible and variable than classical ones, and, thus, raise the problem of predicting which activities will be carried out next, a problem that is not so compelling in classical fields. When the process model is learned automatically from examples of process executions, which is the task of Process Mining, the prediction performance may also provide indirect indications on the correctness and reliability of the learned model. This paper proposes and compares two strategies for activity prediction using the WoMan framework for workflow management. The former proved to be able to handle complex processes, the latter is based on the classic and consolidated Naïve Bayes approach. An experimental validation allows us to draw considerations on the pros and cons of each, used both in isolation and in combination.

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Fig. 1

Notes

  1. 1.

    It can be of any representation of time points (e.g., milliseconds from process case start, date-time in YYYYMMDDHHMMSS format, progressive integers, etc).

  2. 2.

    In First-order logic, the p/n notation is used to denote an n-ary predicate symbol p.

  3. 3.

    Note that this is a different meaning than in Petri Nets.

  4. 4.

    In this perspective the term resource assumes a general meaning, which is not relative to the event resource R that executes an activity in a process case.

  5. 5.

    It is a multiset since an activity could occur more than once.

  6. 6.

    This is a relevant difference with respect to the heuristic approach, where there is no distinction about different occurrences of the same task. Our expectation is that, by considering such a more fine-grained information, predictions may be more accurate.

  7. 7.

    In each case, a relationship between a γ and a 𝜃 occurs at most once, if any.

  8. 8.

    http://ailab.wsu.edu/casas/datasets.html

  9. 9.

    http://www.giraffplus.eu

  10. 10.

    http://scacchi.qnet.it

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Ferilli, S., Angelastro, S. Activity prediction in process mining using the WoMan framework. J Intell Inf Syst 53, 93–112 (2019). https://doi.org/10.1007/s10844-019-00543-2

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Keywords

  • Process mining
  • Activity prediction
  • Process model