Process mining: a two-step approach to balance between underfitting and overfitting

Abstract

Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset. Therefore, classical synthesis techniques are not suitable as they aim at finding a model that is able to exactly reproduce the log. Existing process mining techniques try to avoid such "overfitting" by generalizing the model to allow for more behavior. This generalization is often driven by the representation language and very crude assumptions about completeness. As a result, parts of the model are "overfitting" (allow only for what has actually been observed) while other parts may be "underfitting" (allow for much more behavior without strong support for it). None of the existing techniques enables the user to control the balance between "overfitting" and "underfitting". To address this, we propose a two-step approach. First, using a configurable approach, a transition system is constructed. Then, using the "theory of regions", the model is synthesized. The approach has been implemented in the context of ProM and overcomes many of the limitations of traditional approaches.

Discovery of Frequent Episodes in Event Logs

“…Several variants of the α algorithm have been proposed [12,13]. Moreover, completely different approaches have been proposed, e.g., the different types of genetic process mining [14,15], techniques based on state-based regions [16,17], and techniques based on language-based regions [18,19]. Another, more recent, approach is inductive process mining where the event log is split recursively [20].…”

“…+ -+ --Leemans M., Episode discovery + -n.a. + -+ -+ Van der Aalst, α-algorithm [10] + + -+ + + --Weijters, Heuristics mining [11] + + -+ + + --De Medeiros, Genetic mining [14,15] + + -+ + + + + Solé, State Regions [16,17] + + -+ + + --Bergenthum, Language Regions [18,19] + + -+ + + --Leemans S.J.J., Inductive [20] + + + + + + + - Table 1. Feature comparison of discussed discovery algorithms…”

Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital

“…Finally, we have considered techniques from the process mining field, such as the alpha-algorithm proposed by Van der Aalst et al, the Little Thumb algorithm, InWolve and the suite of algorithms provided by the ProM framework [12,13,14,15,16,17,18,19,20,21,22]. We have found these algorithms to be unsuitable for our goals, as they do not deal with cycles in the data [23].…”

Process Model Discovery: A Method Based on Transition System Decomposition

“…We also follow this approach. Methods for building transition systems based on event logs were proposed in [6] and are out of the scope of this paper. In this paper we assume that there is a transition system, already constructed from some event log, and concentrate on discovering a process model from a given transition system.…”

“…An alternative generalization was proposed by J. Carmona et al [11]. The application of statebased region algorithms to process mining was studied in [6,9,21]. Algorithms based on regions of languages were presented in [7,14,18] and then applied to process mining [8,24].…”