Faster Algorithms for Searching Relevant Matches in XML Databases

Abstract

Keyword search is a friendly mechanism for the end user to identify interesting nodes in XML databases, and the SLCA (smallest lowest common ancestor)-based keyword search is a popular concept for locating the desirable subtrees corresponding to the given query keywords. However, it does not evaluate the importance of each node under those subtrees. Liu and Chen proposed a new concept contributor to output the relevant matches instead of all the keyword nodes. In this paper, we propose two methods, MinMap and SingleProbe, that improve the efficiency of searching the relevant matches by avoiding unnecessary index accesses. We analytically and empirically demonstrate the efficiency of our approaches. According to our experiments, both approaches work better than the existing one. Moreover, SingleProbe is generally better than MinMap if the minimum frequency and the maximum frequency of the query keywords are close.