A Preprocessing Technique for Keyword-Driven Analytical Processing

Keyword-Driven Analytical Processing (KDAP) integrates the simplicity of keyword search with the aggregation power in OLAP (Online-Analytical Processing), which provides an easy-to-use solution to organize the data in a way that a business analyst needs for thinking about the data. For any user query, the system generates Candidate Subspace temporarily through a breadth-first traversal of schema graph. As the number of query keywords increases or the data warehouse schema becomes complicated, time spent on generating Candidate Subspace may increase rapidly. In this paper, we propose a preprocessing technique for candidate subspace generation to support efficient KDAP. The novel approach reduces query time by doing all significant works in advance. It preprocesses the schema graph to generate all schema graphs of Candidate Subspace (G_cs) and then stores them in the database. When a user issues a keyword query, proper G_css are quickly retrieved from the database. Candidate Subspace can be generated from corresponding G_cs. We demonstrate using several experiments that our approach is efficient.