Efficient Continual Top-$k$ Keyword Search in Relational Databases

TL;DR

This paper introduces an efficient approach for continual top-$k$ keyword search in relational databases, enabling real-time updates and relevance maintenance as databases evolve, which is crucial for long-term user interests.

Contribution

It presents a novel two-algorithm framework that efficiently maintains top-$k$ keyword search results over dynamic, frequently updated relational databases.

Findings

The method significantly reduces computation time for continual queries.

Experimental results show high accuracy and efficiency in dynamic environments.

The approach outperforms existing static snapshot methods.

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 data 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 database systems to support continual keyword queries evaluation. In this paper, we propose an efficient method for continual keyword queries answering 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 ranges of the future relevance score for every query result and create a light-weight state for each keyword query. The second one uses these states to maintain the top-$k$ results of keyword queries when the database is continually growing. Experimental results validate the effectiveness and efficiency of the proposed method.