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

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.