Data & Knowledge Engineering
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We consider an environment where a subscription system continuously evaluates pattern-based requests over unbounded sequential data. We propose an extension of the traditional pattern-matching techniques for efficiently handling large sets of such continuous queries. This extension relies on the introduction of variables in patterns in order to augment their expressivity. Based on this extended class of parameterized queries, our main contributions are threefold. First, we define a refinement relation based on variable relaxation. Second, we use the semi-lattice structure of the set of parameterized patterns for patterns aggregation and filtering. We propose an on-line pattern aggregation algorithm so as to both reduce the cost of pattern-matching evaluation as well as to filter out sequences that cannot match any of the patterns in a subscription cluster. Finally we show, through analysis and experiments, that our technique reduces quite effectively the cost of the matching process.