FINCH: evaluating reverse k-Nearest-Neighbor queries on location data
Proceedings of the VLDB Endowment
Location-dependent query processing: Where we are and where we are heading
ACM Computing Surveys (CSUR)
Lazy updates: an efficient technique to continuously monitoring reverse kNN
Proceedings of the VLDB Endowment
ESA: an efficient and stable approach to querying reverse k-nearest-neighbor of moving objects
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Top-k most influential locations selection
Proceedings of the 20th ACM international conference on Information and knowledge management
Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient algorithms to monitor continuous constrained k nearest neighbor queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
Continuous maximal reverse nearest neighbor query on spatial networks
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A branch and bound method for min-dist location selection queries
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
| Hi-index | 0.00 |
The processing of a Continuous Reverse k-Nearest-Neighbor (CRkNN) query on moving objects can be divided into two sub tasks: continuous filter, and continuous refinement. The algorithms for the two tasks can be completely independent. Existing CRkNN solutions employ Continuous k-Nearest-Neighbor (CkNN) queries for both continuous filter and continuous refinement. We analyze the CkNN based solution and point out that when k1 the refinement cost becomes the system bottleneck. We propose a new continuous refinement method called CRange-k. In CRange-k, we transform the continuous verification problem into a Continuous Range-k query, which is also defined in this paper, and process it efficiently. Experimental study shows that the CRkNN solution based on our CRange-k refinement method is more efficient and scalable than the state-of-the-art CRkNN solution.