Seal: spatio-textual similarity search
Proceedings of the VLDB Endowment
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient safe-region construction for moving top-K spatial keyword queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Keyword-based k-nearest neighbor search in spatial databases
Proceedings of the 21st ACM international conference on Information and knowledge management
Star-Join: spatio-textual similarity join
Proceedings of the 21st ACM international conference on Information and knowledge management
Moving spatial keyword queries: Formulation, methods, and analysis
ACM Transactions on Database Systems (TODS)
Spatial keyword query processing: an experimental evaluation
Proceedings of the VLDB Endowment
Scalable top-k spatial keyword search
Proceedings of the 16th International Conference on Extending Database Technology
TsingNUS: a location-based service system towards live city
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Location-aware publish/subscribe
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
G-tree: an efficient index for KNN search on road networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
DART: an efficient method for direction-aware bichromatic reverse k nearest neighbor queries
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
| Hi-index | 0.00 |
Location-based services (LBS) have been widely accepted by mobile users. Many LBS users have direction-aware search requirement that answers must be in the search direction. However to the best of our knowledge there is not yet any research available that investigates direction-aware search. A straightforward method first finds candidates without considering the direction constraint, and then generates the answers by pruning those candidates which invalidate the direction constraint. However this method is rather expensive as it involves a lot of useless computation on many unnecessary directions. To address this problem, we propose a direction-aware spatial keyword search method which inherently supports direction-aware search. We devise novel direction-aware indexing structures to prune unnecessary directions. We develop effective pruning techniques and search algorithms to efficiently answer a direction-aware query. As users may dynamically change their search directions, we propose to incrementally answer a query. Experimental results on real datasets show that our method achieves high performance and outperforms existing methods significantly.