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Effective management of Web Services systems relies on accurate understanding of end-to-end transaction flows, which may change over time as the ser-vice 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. The paper classifies 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 at each step along the in-vocation path, one can recover the end-to-end flow for every executed transaction.
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