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
A semantic analysis of the annotations of the human genome
Bioinformatics
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
Confidence-Aware Join Algorithms
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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We introduce the proximity rank join problem, where we are given a set of relations whose tuples are equipped with a score and a real-valued feature vector. Given a target feature vector, the goal is to return the K combinations of tuples with high scores that are as close as possible to the target and to each other, according to some notion of distance or dissimilarity. The setting closely resembles that of traditional rank join, but the geometry of the vector space plays a distinctive role in the computation of the overall score of a combination. Also, the input relations typically return their results either by distance from the target or by score. Because of these aspects, it turns out that traditional rank join algorithms, such as the well-known HRJN, have shortcomings in solving the proximity rank join problem, as they may read more input than needed. To overcome this weakness, we define a tight bound (used as a stopping criterion) that guarantees instance optimality, that is, an I/O cost is achieved that is always within a constant factor of optimal. The tight bound can also be used to drive an adaptive pulling strategy, deciding at each step which relation to access next. For practically relevant classes of problems, we show how to compute the tight bound efficiently. An extensive experimental study validates our results and demonstrates significant gains over existing solutions.