On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Dataflow query execution in a parallel main-memory environment
PDIS '91 Proceedings of the first international conference on Parallel and distributed information systems
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Maintaining Sliding Window Skylines on Data Streams
IEEE Transactions on Knowledge and Data Engineering
Safety guarantee of continuous join queries over punctuated data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Window-aware load shedding for aggregation queries over data streams
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
Skyline query processing over joins
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
PrefJoin: An efficient preference-aware join operator
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Skyline-sensitive joins with LR-pruning
Proceedings of the 15th International Conference on Extending Database Technology
Layered processing of skyline-window-join (SWJ) queries using iteration-fabric
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Efficient processing of skyline queries has been an area of growing interest over both static and stream environments. Most existing static and streaming techniques assume that the skyline query is applied to a single data source. Unfortunately, this is not true in many applications in which, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple data sources. Recently, in the context of static environments, various hybrid skyline-join algorithms have been proposed. However, these algorithms suffer from several drawbacks: they often need to scan the data sources exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. On the other hand, most existing streaming methods focus on single stream skyline analysis, thus rendering these techniques unsuitable for applications that require a real-time "join" operation to be carried out before the skyline query can be answered. Based on these observations, we introduce and propose to demonstrate SkySuite: a framework of skyline-join operators that can be leveraged to efficiently process skyline-join queries over both static and stream environments. Among others, SkySuite includes (1) a novel Skyline-Sensitive Join (SSJ) operator that effectively processes skyline-join queries in static environments, and (2) a Layered Skyline-window-Join (LSJ) operator that incrementally maintains skyline-join results over stream environments.