Efficient Type-Ahead Search on Relational Data: a TASTIER Approach

Existing keyword-search systems in relational databases require users to submit a complete query to compute answers. Often users feel "left in the dark" when they have limited knowledge about the data, and have to use a try-and-see method to modify queries and find answers. In this paper we propose a novel approach to keyword search in the relational world, called TASTIER. A TASTIER system can bring instant gratification to users by supporting type-ahead search, which finds answers "on the fly" as the user types in query keywords. A main challenge is how to achieve a high interactive speed for large amounts of data in multiple tables, so that a query can be answered efficiently within milliseconds. We propose efficient index structures and algorithms for finding relevant answers on-the-fly by joining tuples in the database. We devise a partition-based method to improve query performance by grouping relevant tuples and pruning irrelevant tuples efficiently. We also develop a technique to answer a query efficiently by predicting highly relevant complete queries for the user. We have conducted a thorough experimental evaluation of the proposed techniques on real data sets to demonstrate the efficiency and practicality of this new search paradigm.