Efficient Processing of Ranked Queries with Sweeping Selection

Wen Jin, Martin Ester, Jiawei Han

Abstract: Existing methods for top-k ranked query employ techniques including sorting, updating thresholds and materializing views. In this paper, we propose two novel index-based techniques for top-k ranked query: (1) indexing the layered skyline, and (2) indexing microclusters of objects into a grid structure. We also develop efficient algorithms for ranked query by locating the answer points during the sweeping of the line/hyperplane of the score function over the indexed objects. Both methods can be easily plugged into typical multi-dimensional database indexes. The comprehensive experiments not only demonstrate that our methods outperform the existing ones, but also illustrate that the application of data mining technique (microclustering) is a useful and effective solution for database query processing.