Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Discovery of Influence Sets in Frequently Updated Databases
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Nearest and reverse nearest neighbor queries for moving objects
The VLDB Journal — The International Journal on Very Large Data Bases
Reverse Nearest Neighbor Search in Metric Spaces
IEEE Transactions on Knowledge and Data Engineering
Multidimensional reverse kNN search
The VLDB Journal — The International Journal on Very Large Data Bases
Reverse nearest neighbor aggregates over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Visible Reverse k-Nearest Neighbor Query Processing in Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
Toward context and preference-aware location-based services
Proceedings of the Eighth ACM International Workshop on Data Engineering for Wireless and Mobile Access
Incremental Evaluation of Visible Nearest Neighbor Queries
IEEE Transactions on Knowledge and Data Engineering
Reverse k-Nearest Neighbor monitoring on mobile objects
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
PutMode: prediction of uncertain trajectories in moving objects databases
Applied Intelligence
Ubiquitous Advertising: The Killer Application for the 21st Century
IEEE Pervasive Computing
Challenges and business models for mobile location-based services and advertising
Communications of the ACM
Spatial Network RNN Queries in GIS
The Computer Journal
TagSense: a smartphone-based approach to automatic image tagging
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Monochromatic and Bichromatic Reverse Top-k Queries
IEEE Transactions on Knowledge and Data Engineering
Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
DESKS: Direction-Aware Spatial Keyword Search
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
| Hi-index | 0.00 |
This paper presents a novel type of queries in spatial databases, called the direction-aware bichromatic reverse k nearest neighbor (DBRkNN) queries,which extend the bichromatic reverse nearest neighbor queries.Given two disjoint sets, P and S, of spatial objects, and a query object q in S, the DBRkNN query returns a subset P′ of P such that k nearest neighbors of each object in P′ include q and each object in P′ has a direction toward q within a pre-defined distance.We formally define the DBRkNN query, and then propose an efficient algorithm, called DART, for processing the DBRkNN query. Our method utilizes a grid-based index to cluster the spatial objects, and the B+-tree to index the direction angle.We adopt a filter-refinement framework that is widely used in many algorithms for reverse nearest neighbor queries. In the filtering step,DART eliminates all the objects that are away from the query object more than the pre-defined distance, or have an invalid direction angle. In the refinement step, remaining objects are verified whether the query object is actually one of the k nearest neighbors of them. From extensive experiments, we show that DART outperforms an R-tree-based naive algorithm in both indexing time and query processing time.