Grid-Index Algorithm for Reverse Rank Queries

In Rank-aware query processing, reverse rank queries have already attracted significant interests. Reverse rank queries can find matching customers for a given product based on individual customers’ preference. The results are used in numerous real-life applications, such as market analysis and product placement. Efficient processing of reverse rank queries is challenging because it needs to consider the combination on the given data set of user preferences and the data set of products. Currently, there are two typical reverse rank queries: Reverse top-k and reverse k-ranks. Both prefer top-ranking products and the most efficient algorithms for them have a common methodology that indexes and prunes the data set using R-trees. This kind of tree-based algorithms suffers the problem that their performance in high-dimensional data declines sharply while high-dimensional data are significant for real-life applications. In this paper, we propose an efficient scan algorithm, named Grid-index algorithm (GIR), for processing reverse rank queries efficiently. GIR algorithm uses an approximate values index to save computations in scanning and only requires a little memory cost. Our theoretical analysis guarantees the efficiency and the experimental results confirm that GIR has superior performance compared to tree-based methods in high-dimensional applications. CCS Concepts •Theory of computation → Database query processing and optimization (theory); c ©2017, Copyright is with the authors. Published in Proc. 20th International Conference on Extending Database Technology (EDBT), March 21-24, 2017 Venice, Italy: ISBN 978-3-89318-073-8, on OpenProceedings.org. Distribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0 Figure 1: Example for RTK and RKR queries. (a): the top2 cell phones appreciated by users. (b): the RT-2 of each phone. (c): the rank list and the R1-R of each phone.