CTrace: semantic comparison of multi-granularity process traces

Authors:
Qing Liu;Kerry Taylor;Xiang Zhao;Geoffrey Squire;Xuemin Lin;Corne Kloppers;Richard Miller
Affiliations:
Intelligent Sensing and System Lab, CSIRO, Hobart, Australia;Information Engineering Lab, CSIRO, Canberra, Australia;University of New South Wales, Sydney, Australia;Information Engineering Lab, CSIRO, Canberra, Australia;University of New South Wales, Sydney, Australia;Intelligent Sensing and Systems Lab, CSIRO, Hobart, Australia;Intelligent Sensing and Systems Lab, CSIRO, Hobart, Australia
Venue:
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Year:
2013

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Abstract

A process trace describes the processes taken in a workflow to generate a particular result. Given many process traces, each with a large amount of very low level information, it is a challenge to make process traces meaningful to different users. It is more challenging to compare two complex process traces generated by heterogenous systems and have different levels of granularity. We present CTrace, a system that (1) lets users explore the conceptual abstraction of large process traces with different levels of granularity, and (2) provides semantic comparison among traces in which both the structural and the semantic similarity are considered. The above functions are underpinned by a novel notion of multi-granularity process trace and efficient multi-granularity similarity comparison algorithms.