Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Algorithms for processing K-closest-pair queries in spatial databases
Data & Knowledge Engineering
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
Processing Distance Join Queries with Constraints
The Computer Journal
Depth estimation for ranking query optimization
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Evaluating rank joins with optimal cost
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Weighted Proximity Best-Joins for Information Retrieval
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Robust and efficient algorithms for rank join evaluation
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Proceedings of the VLDB Endowment
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Rank join can be generalized to sets of relations whose objects are equipped with a score and a real-valued feature vector. Such vectors can be used to compare the objects to one another so as to join them based on a notion of "proximity". The problem becomes then that of retrieving combinations of objects that have high scores, whose feature vectors are close to one another and possibly to a given feature vector (the query). Traditional rank join algorithms may read more input than needed when solving proximity rank join. Such weakness can be overcome by designing new algorithms for which, as in classical rank join, bounding scheme (and a tight version thereof) and pulling strategy play a crucial role to efficiently compute the solution.