Abstract
Web services are typically involved in various types of interaction during their lifespan. They may participate as components in more complex services (composition) or replace unavailable services (substitution). Identifying the invocations that are part of the same interaction relationship and the nature of these relationships provides support for mashup developers. In this paper, we propose a novel approach for discovering composition and substitution relationships from service logs. We introduce a technique to correlate events that are part of the same relationship. We use association rule algorithms to determine the most frequent item-sets of correlated events. We infer composition and substitution relationships from these item-sets and derive a multi-relation network of Web services. Experiments show that 80% of the interaction relationships can be learned with 70% precision.
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Labbaci, H., Medjahed, B., Aklouf, Y. (2017). Learning Interactions from Web Service Logs. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_22
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DOI: https://doi.org/10.1007/978-3-319-64471-4_22
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