Efficient Continual Top-k Keyword Search in Relational Databases

Abstract

Keyword search in relational databases has been widely studied in recent years because it requires users neither to master a certain structured query language nor to know the complex underlying database schemas. Most existing methods focus on answering snapshot keyword queries in static databases. In practice, however, databases are updated frequently, and users may have long-term interests on specific topics. To deal with such situations, it is necessary to build effective and efficient facilities in a database system to support continual keyword queries. In this paper, we propose an efficient method for answering continual keyword queries over relational databases. The proposed method consists of two core algorithms. The first one computes a set of potential top-k results by evaluating the range of the future relevance score for every query result and creates a lightweight state for each keyword query. The second one uses these states to maintain the top-k results of keyword queries while the database is continually being updated. Experimental results validate the effectiveness and efficiency of the proposed method.

A Metadata Search Approach to Keyword Query in Relational Databases

“…Liu et al [21] improves the ranking strategy in [13] by identifies four normalization factors, tuple tree size, document length, document frequency, and inter-document weight. Yanwei et al [22] studies the problem of finding the top-k results in relational databases for a continual keyword query. A set of potential top-k results is computed by evaluating the range of the future relevance score for every query result and a light-weight state is created for each keyword query.…”