The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An index structure for improving nearest closest pairs and related join queries in spatial databases
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Algorithms for processing K-closest-pair queries in spatial databases
Data & Knowledge Engineering
k-Closest Pair Query Monitoring Over Moving Objects
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Probabilistic Group Nearest Neighbor Queries in Uncertain Databases
IEEE Transactions on Knowledge and Data Engineering
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Computation and Monitoring of Exclusive Closest Pairs
IEEE Transactions on Knowledge and Data Engineering
Query ranking in probabilistic XML data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Probabilistic Threshold Range Aggregate Query Processing over Uncertain Data
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Database Support for Probabilistic Attributes and Tuples
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic nearest-neighbor query on uncertain objects
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Efficient fuzzy top-k query processing over uncertain objects
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Efficient and effective similarity search over probabilistic data based on earth mover's distance
Proceedings of the VLDB Endowment
Probabilistic similarity join on uncertain data
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Evaluating probabilistic spatial-range closest pairs queries over uncertain objects
WAIM'11 Proceedings of the 12th international conference on Web-age information management
| Hi-index | 0.00 |
An important topic in the field of spatial data management is processing the queries involving uncertain locations. This paper focuses on the problem of finding probabilistic K closest pairs between two uncertain spatial datasets, namely, Top-K probabilistic closest pairs (TopK-PCP) query, which has popular usages in real applications. Specifically, given two uncertain datasets in which each spatial object is modeled by a set of sample points, a TopK-PCP query retrieves the pairs with top K maximal probabilities of being the closest pair. Due to the inherent uncertainty of data objects, previous techniques to answer K-closest pairs (K-CP) queries cannot be directly applied to our TopK-PCP problem. Motivated by this, we propose a novel method to evaluate TopK-PCP query effectively. Extensive experiments are performed to demonstrate the effectiveness of our method.