Discovering Structured Event Logs from Unstructured Audit Trails for Workflow Mining

Workflow mining aims to find graph-based process models based on activities, emails, and various event logs recorded in computer systems. Current workflow mining techniques mainly deal with well-structured and -symbolized event logs. In most real applications where workflow management software tools are not installed, these structured and symbolized logs are not available. Instead, the artifacts of daily computer operations may be readily available. In this paper, we propose a method to map these artifacts and content-based logs to structured logs so as to bridge the gap between the unstructured logs of real life situations and the status quo of workflow mining techniques. Our method consists of two tasks: discovering workflow instances and activity types. We use a clustering method to tackle the first task and a classification method to tackle the second. We propose a method to combine these two tasks to improve the performance of two as a whole. Experimental results on simulated data show the effectiveness of our method.Peer reviewed: YesNRC publication: Ye

Combination usage of process mining and adaptive case management

Process mining performs well on structured processes like BPM. In recent years, Adaptive Case Management (ACM) was introduced to support knowledge workers by giving them the permission to define processes on the fly in unpredictable situations. By doing so, processes seem to be unstructured and hard to be enhanced. The goal of this thesis is to analyze how process mining can improve and benefit unstructured processes and to develop a prototype for a potential application. The scenario analysis focuses on (1) supporting knowledge workers with process mining, (2) transiting from unstructured to structured processes through process mining, and (3) compliance regulations in unstructured processes through process mining. One scenario is selected based on the analysis for implementing a prototype. Due to the importance of compliance regulations and rare researches on compliance regulations in unstructured processes, the scenario (3) is selected and an approach of compliance checking for unstructured processes using process mining is proposed. The prototype of the approach leverages process mining to discover hidden structured in unstructured processes and implements compliance checking functionalities consisting of graphical rule definition, rule creation and rule checking on a log in Oryx platform. The prototype visualizes violating paths on the process model and reports all violating process instances. The evaluation proves that the prototype is indeed capable of compliance checking with a large real-life data log