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...

• Type: Conference
• Year: 2009
• Where: ISMIS
• Authors: Liqiang Geng, Scott Buffett, Bruce Hamilton, Xin Wang, Larry Korba, Hongyu Liu, Yunli Wang
• Keywords: Artificial Intelligence, Event Logs, ISMIS 2009, Workflow Mining, Workflow Mining Techniques