SSDBM'06 (abstract)
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 efciently 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 dene a
renement relation based on variable relaxation. Second,
we use the semi-lattice structure of the set of parameterized
patterns for patterns aggregation and ltering. We propose
an on-line pattern aggregation algorithm so as to both reduce
the cost of pattern-matching evaluation as well as to
lter 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.
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