Scalable Continual Top-k Keyword Search in Relational Databases

TL;DR

This paper introduces an efficient method for continual top-k keyword search in relational databases, supporting dynamic updates and long-term user interests, with improved performance over existing approaches.

Contribution

It extends existing keyword search techniques by incorporating ranking mechanisms for continual queries in relational databases, enhancing efficiency in static and dynamic scenarios.

Findings

The proposed method outperforms existing techniques in efficiency.

It effectively maintains top-k results amid database updates.

Experimental results confirm its effectiveness.

Abstract

Keyword search in relational databases has been widely studied in recent years because it does not require users neither to master a certain structured query language nor to know the complex underlying database schemas. Most of 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 a situation, it is necessary to build effective and efficient facility in a database system to support continual keyword queries. In this paper, we propose an efficient method for answering continual top-$k$ keyword queries over relational databases. The proposed method is built on an existing scheme of keyword search on relational data streams, but incorporates the ranking mechanisms into the query processing methods and makes two improvements to support efficient top-$k$ keyword search in relational databases. Compared to the existing methods, our method is more efficient both in computing the top-$k$ results in a static database and in maintaining the top-$k$ results when the database continually being updated. Experimental results validate the effectiveness and efficiency of the proposed method.