Name
Abstract | ||
|---|---|---|
Recently, Reverse k Nearest Neighbors RkNN queries, returning every answer for which the query is one of its k nearest neighbors, have been extensively studied on the database research community. But the RkNN query cannot retrieve spatio-textual objects which are described by their spatial location and a set of keywords. Therefore, researchers proposed a RSTkNN query to find these objects, taking both spatial and textual similarity into consideration. However, the RSTkNN query cannot control the size of answer set and to be sorted according to the degree of influence on the query. In this paper, we propose a new problem Ranked Reverse Boolean Spatial Keyword Nearest Neighbors query called Ranked-RBSKNN query, which considers both spatial similarity and textual relevance, and returns t answers with most degree of influence. We propose a separate index and a hybrid index to process such queries efficiently. Experimental results on different real-world and synthetic datasets show that our approaches achieve better performance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26190-4_7 | WISE |
Field | DocType | Citations |
k-nearest neighbors algorithm,Query optimization,Web search query,Data mining,Query expansion,Information retrieval,Computer science,Sargable,Web query classification,Spatial query,Database,Boolean conjunctive query | Conference | 3 |
PageRank | References | Authors |
0.37 | 25 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hailin Fang | 1 | 3 | 0.37 |
| Pengpeng Zhao | 2 | 111 | 30.91 |
| Victor S. Sheng | 3 | 1511 | 108.09 |
| Zhixu Li | 4 | 210 | 43.55 |
| Jiajie Xu | 5 | 58 | 10.08 |
| Jian Wu | 6 | 88 | 13.75 |
| zhiming cui | 7 | 23 | 8.62 |