Name
Abstract | ||
|---|---|---|
In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q; l; w; n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several properties and accordingly develop an NWC algorithm. Moreover, we propose several optimization techniques to further reduce the search cost. To validate our ideas, we conduct a comprehensive performance evaluation using both real and synthetic datasets. Experimental results show that the proposed NWC algorithm, along with the optimization techniques, is very efficient under various datasets and parameter settings. |
Year | Venue | Field |
|---|---|---|
2016 | EDBT | Data mining,Computer science,Search cost,Database |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
14 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen-Che Huang | 1 | 43 | 4.82 |
| Jiun-Long Huang | 2 | 592 | 47.09 |
| Tsung-Ching Liang | 3 | 1 | 0.35 |
| Jun-Zhe Wang | 4 | 35 | 2.82 |
| Wen-Yuah Shih | 5 | 64 | 6.23 |
| Wang-Chien Lee | 6 | 5765 | 346.32 |