Root Rank

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 a detailed empirical study, 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. In this paper, we investigate addressing the RSD challenge through inclusion of custom operators within the database engine. Specifically, we propose and evaluate an operator called Root Rank, which performs result ranking in the root of the query execution plan.The Root Rank operator has been implemented on a PostgreSQL codebase, and its performance profiled over real-world data sets, including DBLP and Wikipedia. Our experimental observations indicate that the Root Rank operator is highly successful in 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.