Combining fuzzy information from multiple systems
Journal of Computer and System Sciences
Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient top-k aggregation of ranked inputs
ACM Transactions on Database Systems (TODS)
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
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
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In the rank join problem, we are given a set of relations and a scoring function, and the goal is to return the K join results with the highest scores. It is often the case in practice that the inputs may be accessed in ranked order and the scoring function is monotonic. These conditions allow for efficient algorithms that solve the rank join problem without reading all of the input. In this chapter, we review recent efforts in the development and analysis of such rank join algorithms. First, we present some theoretical results that state the inherent complexity of the rank join problem and essentially reveal that any rank join algorithm has to trade off between I/O efficiency and computational efficiency. We then review a specific rank join algorithm that adjusts this trade-off at runtime, depending on the data and the scoring function, in order to strike a balance between I/O overhead and computation.