Efficient Process Model Discovery Using Maximal Pattern Mining

Abstract

Abstract. In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses to understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces-ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better recall, precision, F-measure and efficiency than the existing techniques. Furthermore, by using the MPM, the discovered models are generally much easier to understand.

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

“…In [71], the authors propose a method for automated process discovery using Maximal Pattern Mining where they discover recurrent sequences of events in the traces of the log. Starting from these patterns they build process models in the form of causal nets.…”

“…[19], [25], [26], [33], [67], [68], [69], [75], [77], [87], [93]) were further tested against synthetic logs, while 13 approaches (cf. [12], [16], [19], [55], [56], [63], [68], [71], [72], [79], [83], [84], [96]) against artificial logs. Finally, one method was tested both on synthetic and artificial logs only (cf.…”

“…Concerning the application domains of the real-life logs, we noticed that several methods used a selection of the logs made available by the Business Process Intelligence Challenge (BPIC), which is held annually as part of the BPM Conference series. 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.…”

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

“…Most of the more recent and more sophisticated process discovery methods support noise filtering [3]. Existing noise-filtering methods are based on frequencies [7,8,9,10], machine-learning techniques [11,12], genetic algorithms [13], or probabilistic models [14,15]. All of those methods focus on the controlflow perspective (i.e., the event labels) when filtering noise.…”

Generating time-based label refinements to discover more precise process models

“…The aim of process discovery is to discover a process model based on and event log L that has both high fitness (i.e., it allows for the behavior seen in the log) and high precision (i.e., it does not allow for too much behavior that was not seen in the log). Many process discovery algorithms have been proposed throughout the years, including techniques based on Integer Linear Programming and the theory of regions [18], Inductive Logic Programming [19], maximal pattern mining [20], or based on heuristic techniques [21,22]. We refer the reader to [1] for a thorough introduction of several process discovery techniques.…”