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Tsinghua Science and Technology

Keywords

web service, service deprecation predict, Latent Dirichlet Allocation (LDA), extreme learning machine

Abstract

An increasing number of web services are being invoked by users to create user applications (e.g., mashups). However, over time, a few good services in service systems have become deprecated, i.e., the service is initially available and is invoked by service users, but later becomes unavailable. Therefore, the prediction of service deprecation has become a key issue in creating reliable long-term user applications. Most existing research has overlooked service deprecation in service systems and has failed to consider long-term service reliability when making service recommendations. In this paper, we propose a method for predicting service deprecation, which comprises two components: Service Comprehensive Feature Modeling (SCFM) for extracting service features relevant to service deprecation and Deprecated Service Prediction (DSP) for building a service deprecation prediction model. Our experimental results on a real-world dataset demonstrate that our method yields an improved Area-Under-the-Curve (AUC) value over existing methods and thus has better accuracy in service deprecation prediction.

Publisher

Tsinghua University Press

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