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Mathematical Problems in Engineering
Volume 2015, Article ID 325192, 15 pages
http://dx.doi.org/10.1155/2015/325192
Research Article

Improving ELM-Based Service Quality Prediction by Concise Feature Extraction

College of Information Science and Engineer, Northeastern University, Shenyang 110819, China

Received 20 August 2014; Revised 10 November 2014; Accepted 12 November 2014

Academic Editor: Jiuwen Cao

Copyright © 2015 Yuhai Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Web services often run on highly dynamic and changing environments, which generate huge volumes of data. Thus, it is impractical to monitor the change of every QoS parameter for the timely trigger precaution due to high computational costs associated with the process. To address the problem, this paper proposes an active service quality prediction method based on extreme learning machine. First, we extract web service trace logs and QoS information from the service log and convert them into feature vectors. Second, by the proposed EC rules, we are enabled to trigger the precaution of QoS as soon as possible with high confidence. An efficient prefix tree based mining algorithm together with some effective pruning rules is developed to mine such rules. Finally, we study how to extract a set of diversified features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that ELM trained by the selected feature subsets can efficiently improve the reliability and the earliness of service quality prediction.