On Efficient Obstructed Reverse Nearest Neighbor Query Processing
In this paper, we study a new form of reverse nearest neighbor (RNN) queries, i.e., obstructed reverse nearest neighbor (ORNN) search. It considers the impact of obstacles on the distance between objects, which is ignored by the existing work on RNN retrieval. Given a data set P, an obstacle set O,...
Saved in:
| Main Authors: | , , , , |
|---|---|
| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
2011
|
| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/1457 http://dx.doi.org/10.1145/2093973.2094000 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | In this paper, we study a new form of reverse nearest neighbor (RNN) queries, i.e., obstructed reverse nearest neighbor (ORNN) search. It considers the impact of obstacles on the distance between objects, which is ignored by the existing work on RNN retrieval. Given a data set P, an obstacle set O, and a query point q in a 2D space, an ORNN query finds all the points/objects in P that have q as their nearest neighbor, according to the obstructed distance metric, i.e., the length of the shortest path between two points without crossing any obstacle. We formalize ORNN search, develop effective pruning heuristics (via introducing a novel boundary region concept), and propose efficient algorithms for ORNN query processing, assuming that both P and O are indexed by traditional data-partitioning indexes (e.g., R-trees). Extensive experiments demonstrate the effectiveness of our developed pruning heuristics and the performance of our proposed algorithms, using both real and synthetic datasets. |
|---|