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

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.