Mining frequent patterns in process models

Abstract

Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models, retrieving a complex one, i.e., a hardly readable process model, can hinder the extraction of information. Even in wellstructured process models, there is information that cannot be obtained with the current techniques. In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, including some from BPI Challenges, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.

Mining Simple Path Traversal Patterns in Knowledge Graph

“…All of them input the same training set and parameters and return the same set of frequent sequential patterns. There are also tons of sequential patterns mining algorithms designed under diverse background recently, such as Skopus [25], uWSequence [28], WoMine [4], UNB [35], etc. The problem definition of our work is different from these works because we aim at mining simple path traversal patterns from a knowledge graph.…”

An operational support approach for Mining Unstructured Business Processes

“…By doing so, an audit report is obtained. However, none of the previous works have demonstrated how to refine the initial normative model, using the combination between frequency paths concept [23] and the simplified BP form of an UBP. Therefore, we propose to combine between the latest auditing version and the frequency paths concept (approach detailed in sub-section 5.1).…”

Automatic linguistic reporting of customer activity patterns in open malls

“…To further analyze the customers' behavior, and to extract activity patterns being executed frequently to focus on the most common behavior, we have used WoMine [5]. WoMine is an algorithm to extract frequent activity patterns -i.e., subprocesses that are executed frequently -from a process model, measuring their frequency in the instances of the log.…”

“…To cope with this shortcoming, a more specific process mining analysis can be run, focusing on the parts of the process that the users are executing more frequently. We have used WoMine [5] to extract the frequent activity patterns -subprocesses -modeling the behavior commonly executed by the users. Figure 9 shows two of the more relevant frequent activity patterns extracted by WoMine.…”