NDLI: A Temporal Data-Mining Approach for Discovering End-to-End Transaction Flows

Content Provider	IEEE Xplore Digital Library
Author	Ting Wang Chang-shing Perng Tao Tao Chungqiang Tang So, E. Chun Zhang Rong Chang Ling Liu
Copyright Year	2008
Description	Author affiliation: T.J. Watson Res. Center, IBM, Yorktown Heights, NY (Chang-shing Perng; Tao Tao; Chungqiang Tang; So, E.; Chun Zhang; Rong Chang) \|\| Georgia Inst. of Technol., Atlanta, GA (Ting Wang; Ling Liu)
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.
Starting Page	37
Ending Page	44
File Size	269057
Page Count	8
File Format	PDF
ISBN	9780769533100
DOI	10.1109/ICWS.2008.59
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Web services Service oriented architecture Computerized monitoring Business Data mining Yarn Technology management Unsupervised learning Humans Noise robustness Transaction Flow Temporal Data Mining
Content Type	Text
Resource Type	Article

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