Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 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
The directional p-median problem with applications to traffic quantization and multiprocessor scheduling
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Medoid queries in large spatial databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Dynamic balanced storage in wireless sensor networks
DMSN '07 Proceedings of the 4th workshop on Data management for sensor networks: in conjunction with 33rd International Conference on Very Large Data Bases
Tree-based partition querying: a methodology for computing medoids in large spatial datasets
The VLDB Journal — The International Journal on Very Large Data Bases
Identifying the Most Endangered Objects from Spatial Datasets
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Efficient method for maximizing bichromatic reverse nearest neighbor
Proceedings of the VLDB Endowment
Optimal matching between spatial datasets under capacity constraints
ACM Transactions on Database Systems (TODS)
Continuous spatial assignment of moving users
The VLDB Journal — The International Journal on Very Large Data Bases
Continuous medoid queries over moving objects
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Finding the sites with best accessibilities to amenities
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Preference-based top-k spatial keyword queries
Proceedings of the 1st international workshop on Mobile location-based service
Maximizing bichromatic reverse nearest neighbor for Lp-norm in two- and three-dimensional spaces
The VLDB Journal — The International Journal on Very Large Data Bases
A scalable algorithm for maximizing range sum in spatial databases
Proceedings of the VLDB Endowment
Location selection for utility maximization with capacity constraints
Proceedings of the 21st ACM international conference on Information and knowledge management
Optimal k-constraint coverage queries on spatial objects
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
A branch and bound method for min-dist location selection queries
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Approximate MaxRS in spatial databases
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
This paper proposes and solves the min-dist optimal-location query in spatial databases. Given a set S of sites, a set O of weighted objects, and a spatial region Q, the min-dist optimal-location query returns a location in Q which, if a new site is built there, minimizes the average distance from each object to its closest site. This query can help a franchise (e.g. McDonald's) decide where to put a new store in order to maximize the benefit to its customers. To solve this problem is challenging, for there are theoretically infinite number of locations in Q, all of which could be candidates. This paper first provides a theorem that limits the number of candidate locations without losing the power to find exact answers. Then it provides a progressive algorithm that quickly suggests a location, tells the maximum error it may have, and keeps refining the result. When the algorithm finishes, the exact answer can be found. The intermediate result of early runs can be used to prune the search space for later runs. Crucial to the pruning technique are novel lower-bound estimators. The proposed algorithm, the effect of several optimizations, and the progressiveness are experimentally evaluated.