Robust and efficient algorithms for rank join evaluation
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Optimal algorithms for evaluating rank joins in database systems
ACM Transactions on Database Systems (TODS)
Probabilistic ranking over relations
Proceedings of the 13th International Conference on Extending Database Technology
Probabilistic string similarity joins
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Search computing: a model-driven perspective
ICMT'10 Proceedings of the Third international conference on Theory and practice of model transformations
Proceedings of the VLDB Endowment
Search computing
Probabilistic threshold join over distributed uncertain data
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Proximity measures for rank join
ACM Transactions on Database Systems (TODS)
TJJE: An efficient algorithm for top-k join on massive data
Information Sciences: an International Journal
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In uncertain and probabilistic databases, confidence values (or probabilities) are associated with each data item. Confidence values are assigned to query results based on combining confidences from the input data. Users may wish to apply a threshold on result confidence values, ask for the "top-$k$'' results by confidence, or obtain results sorted by confidence. Efficient algorithms for these types of queries can be devised by exploiting properties of the input data and the combining functions for result confidences. Previous algorithms for these problems assumed sufficient memory was available for processing. In this paper, we address the problem of processing all three types of queries when sufficient memory is not available, minimizing retrieval cost. We present algorithms, theoretical guarantees, and experimental evaluation.