Abstract
Web users tend to focus their attention on stream data rather than on archival one. In an XML stream environment, data arrive continuously, which means that queries must be evaluated for producing results on the fly. Thus, it is crucial to count on algorithms that efficiently evaluate multiple queries, usually expressed in terms of keywords, over several XML streams. However, there are few algorithms that evaluate this kind of query. One of them is MKStream, which is the current state-of-the-art algorithm for processing keyword-based queries over XML streams. In order to improve scalability, in this paper we introduce PMKStream (Parallel MKStream), which is a parallel version of MKStream. Like MKStream, PMKStream evaluates multiple keyword queries by using multiple parsing stacks. However, it parallelizes them on a multicore platform, thus reducing response time by up to 57%. A comprehensive set of experiments, using distinct datasets, evaluates its performance and shows that PMKStream is a much more efficient alternative for supporting keyword-based search over XML streams.
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