Operator implementation of Result Set Dependent KWS scoring functions

Abstract

A popular approach to hosting Keyword Search Systems (KWS) on relational DBMS platforms is to employ the Candidate Network framework. The quality of a Candidate Network-based search is critically dependent on the scoring function used to rank the relevant answers. In this paper, we first demonstrate, through detailed empirical and conceptual analysis studies, that the Labrador scoring function provides the best user relevance among contemporary Candidate Network scoring functions.Efficiently incorporating the Labrador function, however, is rendered difficult due to its Result Set Dependent (RSD) characteristic, wherein the distribution of keywords in the query results influences the ranking. To address this RSD challenge ►We investigate two mechanisms ►(a) a simple wrapper approach that leverages existing RDBMS functionalities through an SQL wrapper ►And (b) a more sophisticated operator approach wherein the database engine is augmented with custom operators that perform result ranking in the query execution plan.The above strategies have been implemented on a PostgreSQL codebase, inclusive of integration with the optimizer for the operator approach. A detailed empirical study over real-world data sets, including DBLP and Wikipedia, indicates that the wrapper approach addresses the RSD efficiency issue to a limited extent only. More encouragingly, the operator approach is extremely successful, delivering processing times that are comparable to, or better than, those of non-RSD implementations. We expect these results to aid in the organic hosting of KWS functionality on database systems.