Title:
Process Mining, Discovery, and Integration Using Distance Measures
Process Mining, Discovery, and Integration Using Distance Measures
Author(s)
Bae, Joonsoo
Caverlee, James
Liu, Ling
Rouse, William B.
Caverlee, James
Liu, Ling
Rouse, William B.
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Abstract
Business processes continue to play an important
role in today's service-oriented enterprise computing systems.
Mining, discovering, and integrating process-oriented services
has attracted growing attention in the recent year. In this paper
we present a quantitative approach to modeling and capturing the
similarity and dissimilarity between different workflow designs.
Concretely, we introduce a graph-based distance measure and a
framework for utilizing this distance measure to mine the process
repository and discover workflow designs that are similar to a
given design pattern or to produce one integrated workflow
design by merging two or more business workflows of similar
designs. We derive the similarity measures by analyzing the
workflow dependency graphs of the participating workflow
processes. Such an analysis is conducted in two phases. We first
convert each workflow dependency graph into a normalized
process network matrix. Then we calculate the metric space
distance between the normalized matrices. This distance measure
can be used as a quantitative and qualitative tool in process
mining, process merging, and process clustering, and ultimately it
can reduce or minimize the costs involved in design, analysis, and
evolution of workflow systems.
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Date Issued
2006
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Text
Resource Subtype
Technical Report