(Georgia Institute of Technology, 2006)
Bae, Joonsoo; Caverlee, James; Liu, Ling; Rouse, William B.
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.
(Georgia Institute of Technology, 2003)
Caverlee, James; Liu, Ling; Rocco, Daniel J. (Daniel John)
This paper presents a novel source-biased approach to automatically discover and rank relevant data intensive web services. It supports a service-centric view of the Web through source-biased probing and source-biased relevance detection and ranking metrics. Concretely, our approach is capable of answering source-centric queries by focusing on the nature and degree of the topical relevance of one service to others. This source-biased probing allows us to determine in very few interactions whether a target service is relevant to the source by probing the target with very precise probes and then ranking the relevant services discovered based on a set of metrics we define. Our metrics allow us to determine the nature and degree of the relevance of one service to another. We also introduce a performance enhancement to our basic approach called source-biased probing with focal terms. We also extend the basic probing framework to a more generalized service neighborhood graph model. We discuss the semantics of the neighborhood graph, how we may reason about the relationships among multiple services, and how we rank services based on the service neighborhood graph model. We also report initial experiments to show the effectiveness of our approach.