On the representation and querying of sets of possible worlds
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
A greedy algorithm for bulk loading R-trees
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
Robust and efficient fuzzy match for online data cleaning
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Range search on multidimensional uncertain data
ACM Transactions on Database Systems (TODS)
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Ranked Reverse Nearest Neighbor Search
IEEE Transactions on Knowledge and Data Engineering
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE 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 Reverse Nearest Neighbor Queries on Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Efficient evaluation of location-dependent skyline queries using non-dominance scopes
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Continuous inverse ranking queries in uncertain streams
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
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The reverse rank of a (data) object o with respect to a given query object q (that measures the relative nearness of q to o) is said to be k when q is the k-th nearest neighbor of o in a geographical space. Based on the notion of reverse ranks, a Reverse Ranking (RR) query determines t objects with the smallest k's with respect to a given query object q. In many situations that locations of objects and a query object can be imprecise, objects would receive multiple possible k's. In this paper, we propose a notion of expected reverse ranks and evaluation of RR queries over imprecise data based on expected reverse ranks. For any object o, an expected reverse rank kk is a weighted average of possible reverse ranks for individual instances of o with respect to different instances of a given query object q by taking their probabilities into account. We devise and present incremental kk computation and two kk-Estimating algorithms to efficiently evaluate RR queries over imprecise data. The efficiency of our approach is demonstrated through experiments.