Computing dominances inEn (short communication)
Information Processing Letters
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th 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
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approaching the skyline in Z order
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Angle-based space partitioning for efficient parallel skyline computation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Skyline and mapping aware join query evaluation
Information Systems
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
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
SkySuite: a framework of skyline-join operators for static and stream environments
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Efficient processing of skyline queries has been an area of growing interest. Most existing techniques assume that the skyline query is applied to a single data table. Unfortunately, this is not true in many applications where, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple tables. Recently, various hybrid skyline-join algorithms have been proposed. However, the current proposals suffer from several drawbacks: they often need to scan the input tables 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. In this paper, we aim to address these shortcomings by proposing two novel skyline-join algorithms, namely skyline-sensitive join (S 2J) and symmetric skyline-sensitive join (S 3J), to process skyline queries over multiple tables. Our approaches compute the results using a novel layer/region pruning technique (LR-pruning) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise point-to-point dominance checks. Furthermore, the S 3J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. We report extensive experimental results that confirm the advantages of the proposed algorithms over the state-of-the-art skyline-join techniques.