Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs

Abstract

The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-ofthe-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or highly generalize it (low precision). Striking a tradeoff between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method that produces simple process models with low branching complexity and consistently high and balanced fitness, precision and generalization, while achieving execution times 2-6 times faster than state-of-the-art methods on a set of 12 real-life logs. Further, our approach guarantees deadlock-freedom for cyclic process models and soundness for acyclic. Our proposal combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the directly-follows graph.

An interdisciplinary comparison of sequence modeling methods for next-element prediction

“…Therefore, one could say that the uniform distribution matches the graphical representation of the Petri net. The Split Miner [11] is the best performing process discovery technique on two of the four logs when we learn the probability distribution per marking from the training data, with on the other logs the Heuristics Miner [78] and the Inductive Miner with 20% filtering [53] being the best approach. An interesting observation can be made about the Indulpet Miner [54], which does not perform well on average performs but has a very large 95%-CI for all three logs, indicating that for some of the random train/test-splits the method generates quite accurate predictions but for others very inaccurate ones.…”

Automated Discovery of Process Models from Event Logs: Review and Benchmark

“…One of the methods which has a ProM implementation [33] is also available as standalone tool. The works [19], [79], [92], [95] provide both a standalone implementation and a further implementation as a plug-in for Apromore. 2 Apromore is an online process analytics platform, also open source, and has a growing consensus among academics as a process mining tool oriented towards end users.…”

“…These logs are publicly available at the 4TU Centre for Research Data, 4 and cover domains such as healthcare (used by [19], [33], [38], [46], [71], [85], [92]), banking (used by [19], [33], [38], [62], [67], [72], [77], [81], [85], [92], [96]), IT support management in automotive (cf. [19], [75], [77], [92]), and public administration (cf. [16], [26], [38], [55], [96]).…”

“…[16], [26], [38], [55], [96]). A public log pertaining to a process for managing road traffic fines (also available at the 4TU Centre for Research Data) was used in [19], [92]. In [84], the authors use logs from various domains available at http://www.processmining.be/actitrac/.…”

Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

“…Throughout this paper, we use a state-of-the-art discovery algorithm called split miner [37,38] which is a recent technique to discover PNs from event logs. The method has been developed by Augusto et al with the objective to detect models with high fitness and precision, yet low complexity.…”