Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 17th International Conference on Data Engineering
SUBSKY: Efficient Computation of Skylines in Subspaces
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Relaxing join and selection queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
SaLSa: computing the skyline without scanning the whole sky
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Evaluating rank joins with optimal cost
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient sort-based skyline evaluation
ACM Transactions on Database Systems (TODS)
Proceedings of the VLDB Endowment
Skyline-sensitive joins with LR-pruning
Proceedings of the 15th International Conference on Extending Database Technology
Flexible and extensible preference evaluation in database systems
ACM Transactions on Database Systems (TODS)
SkySuite: a framework of skyline-join operators for static and stream environments
Proceedings of the VLDB Endowment
Skyline queries, front and back
ACM SIGMOD Record
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This paper addresses the problem of efficiently computing the skyline set of a relational join. Existing techniques either require to access all tuples of the input relations or demand specialized multi-dimensional access methods to generate the skyline join result. To avoid these inefficiencies, we introduce the novel SFSJ algorithm that fuses the identification of skyline tuples with the computation of the join. SFSJ is able to compute the correct skyline set by accessing only a subset of the input tuples, i.e., it has the property of early termination. SFSJ employs standard access methods for reading the input tuples and is readily implementable in an existing database system. Moreover, it can be used in pipelined execution plans, as it generates the skyline tuples progressively. Additionally, we formally analyze the performance of SFSJ and propose a novel strategy for accessing the input tuples that is proven to be optimal for SFSJ. Finally, we present an extensive experimental study that validates the effectiveness of SFSJ and demonstrates its advantages over existing techniques.