Paper Info
Title
A Temporal Data-Mining Approach for Discovering End-to-End Transaction Flows
Abstract
Effective management of Web Services systems relies on accurate understanding of end-to-end transaction flows, which may change over time as the service composition evolves. This work takes a data mining approach to automatically recovering end-to-end transaction flows from (potentially obscure) monitoring events produced by monitoring tools. We classify the caller-callee relationships among monitoring events into three categories(identity, direct-invoke, and cascaded-invoke), and propose unsupervised learning algorithms to generate rules for each type of relationship. The key idea is to leverage the temporal information available in the monitoring data and extract patterns that have statistical significance. By piecing together the caller-callee relationships a teach step along the invocation path, we can recover the end-to-end flow for every executed transaction. Experiments demonstrate that our algorithms outperform human experts in terms of solution quality, scale well with the data size, and are robust against noises in monitoring data.
Year
DOI
Venue
2008
10.1109/ICWS.2008.59
ICWS
Keywords
Field
DocType
monitoring data,caller-callee relationship,end-to-end flow,data mining approach,effective management,web services system,end-to-end transaction flow,executed transaction,accurate understanding,data size,temporal data-mining approach,discovering end-to-end transaction flows,histograms,servers,web services,web service,computer architecture,unsupervised learning,data mining
Histogram,Data mining,Computer science,End-to-end principle,Server,Unsupervised learning,Database transaction,Web service,Temporal data mining,Transaction data,Database
Conference
Citations 
PageRank 
References 
2
0.51
7
Authors
8
Name
Order
Citations
PageRank
Ting Wang166465.43
Chang-Shing Perng247835.92
Tao Tao3283.85
Chungqiang Tang470.97
Edward So5697.90
Chun Zhang6123.83
Rong Chang7393.43
Ling Liu85020344.35