Name
Abstract | ||
|---|---|---|
Recently, Collective Spatial Keyword Querying (CoSKQ), which returns a group of objects that cover a set of given keywords collectively and have the smallest cost, has received extensive attention in spatial database community. However, no research so far focuses on a situation when the result of CoSKQ is taken as the input of a query. But this kind of query has many applications in location based services. In this paper, we introduce a new problem Reverse Collective Spatial Keyword Querying (RCoSKQ) that returns a region, in which the query objects are qualified objects with the highest spatial and textual similarity. We propose an efficient method which uses IR-tree to retrieve objects with text descriptions. To accelerate the query process, a pruning method that effectively reduces computing is proposed. The experiments over real and synthesis data sets demonstrate the efficiency of our approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-12981-1_15 | CollaborateCom |
Field | DocType | Citations |
Data set,Information retrieval,Computer science,Location-based service,Spatial database,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 13 | 6 |