Abstract
Keyword search is a popular way to discover information from XML data. To return meaningful results, SLCA (smallest lowest common ancestor) has been proposed to identify interesting data nodes. Since the SLCAs are obtained from a series of intermediate LCAs, it can often incur many unnecessary LCA computations even when the size of final results is small. In this paper, we propose an Iterative-Skip approach to address this challenge. We first study the relation between SLCA candidates and propose a series of properties. Then based on these properties, we design a novel skip strategy to skip more SLCA computations. Experimental results show the effectiveness of our approach.
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