Name
Abstract | ||
|---|---|---|
In recent years, spatial-keyword queries have attracted much attention with the fast development of location-based services. However, current spatial-keyword techniques are disk-based, which cannot fulfill the requirements of high throughput and low response time. With the surging data size, people tend to process data in distributed in-memory environments to achieve low latency. In this paper, we present the distributed solution, i.e., Skia (Spatial-Keyword In-memory Analytics), to provide a scalable backend for spatial-textual analytics. Skia introduces a two-level index framework for big spatial-textual data including: (1) efficient and scalable global index, which prunes the candidate partitions a lot while achieving small space budget; and (2) four novel local indexes, that further support low latency services for exact and approximate spatial-keyword queries. Skia can support common spatial-keyword queries via traditional SQL programming interfaces. The experiments conducted on large-scale real datasets have demonstrated the promising performance of the proposed indexes and our distributed solution. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TKDE.2019.2915828 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Distributed systems,indexing,spatial-textual analysis | Journal | 32 |
Issue | ISSN | Citations |
12 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Xu | 1 | 1 | 0.71 |
| Bin Yao | 2 | 365 | 32.71 |
| Zhi Jie Wang | 3 | 34 | 11.30 |
| Xiaofeng Gao | 4 | 713 | 98.58 |
| Jiong Xie | 5 | 0 | 0.34 |
| Minyi Guo | 6 | 3969 | 332.25 |