Discovering Structured Event Logs from Unstructured Audit Trails for Workflow Mining

Authors:
Liqiang Geng;Scott Buffett;Bruce Hamilton;Xin Wang;Larry Korba;Hongyu Liu;Yunli Wang
Affiliations:
IIT, National Research Council of Canada, Fredericton, Canada E3B 9W4;IIT, National Research Council of Canada, Fredericton, Canada E3B 9W4;IIT, National Research Council of Canada, Fredericton, Canada E3B 9W4;Department of Geomatics Engineering, University of Calgary, Canada T2N 1N4;IIT, National Research Council of Canada, Fredericton, Canada E3B 9W4;IIT, National Research Council of Canada, Fredericton, Canada E3B 9W4;IIT, National Research Council of Canada, Fredericton, Canada E3B 9W4
Venue:
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Year:
2009

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Abstract

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.