Efficient Top-k Keyword Search Over Multidimensional Databases

Ziqiang Yu, Xiaohui Yu and Yang Liu
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Ziqiang Yu: School of Computer Science and Technology, Shandong University, Jinan, China
Xiaohui Yu: School of Computer Science and Technology, Shandong University, Jinan, China & School of Information Technology, York University, Toronto, Canada
Yang Liu: School of Computer Science and Technology, Shandong University, Jinan, China

International Journal of Data Warehousing and Mining (IJDWM), 2013, vol. 9, issue 3, 1-21

Abstract: Keyword search over databases has recently received significant attention. Many solutions and prototypes have been developed. However, due to large memory consumption requirements and unpredictable running time, most of them cannot be applied directly to the situations where memory is limited and quick response is required, such as when performing keyword search over multidimensional databases in mobile devices as part of the OLAP functionalities. In this paper, the authors attack the keyword search problem from a new perspective, and propose a cascading top-k keyword search algorithm, which generates supernodes by a branch and bound method in each step of search instead of computing the Steiner trees as done in many existing approaches. This new algorithm consumes less memory and significantly reduces the response time. Experiments show that the method can achieve high search efficiency compared with the state-of-the-art approaches.

Date: 2013
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