Answering Keyword Queries involving Aggregates and GROUPBY on Relational Databases

Abstract

Keyword search over relational databases has gained popularity as it provides a user-friendly way to explore structured data. Current research in keyword search has largely ignored queries to retrieve statistical information from the database. The work in [13] extends keywords by supporting aggregate functions in their SQAK system. However, SQAK does not consider the semantics of objects and relationships in the database, and thus suffers from the problems of returning incorrect answers. In this work, we propose a semantic approach to answer keyword queries involving aggregates and GROUPBY. Our approach utilizes the ORM schema graph to capture the Object-Relationship-Attribute (ORA) semantics in the database, and determines the various interpretations of a query before generating the corresponding SQL statements. These semantics enable us to distinguish objects with the same attribute value and detect duplications of objects in relationships to compute the answers correctly. Our approach can also handle unnormalized relations in the database and GROUPBY in keyword queries which SQAK cannot. Experiments on the TPC-H and ACM Digital Library publication datasets demonstrate the advantages of the proposed semantic approach in retrieving correct statistical information for users.