On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Common expression analysis in database applications
SIGMOD '82 Proceedings of the 1982 ACM SIGMOD international conference on Management of data
Semantic Caching and Query Processing
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 17th International Conference on Data Engineering
Semantic Data Caching and Replacement
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Maximal vector computation in large data sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient computation of the skyline cube
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Refreshing the sky: the compressed skycube with efficient support for frequent updates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Caching Dynamic Skyline Queries
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Caching stars in the sky: a semantic caching approach to accelerate skyline queries
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Caching stars in the sky: a semantic caching approach to accelerate skyline queries
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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Although multi-criteria decision making has emerged with the advent of skyline queries, processing such queries for high dimensional datasets remains a time consuming task. Real-time applications are thus infeasible, especially for non-indexed skyline techniques where the datasets arrive online. In this paper, we propose a caching mechanism that uses the semantics of previous skyline queries to improve the processing time of a new query. In addition to exact queries, such special semantics allow accelerating related queries. We achieve this by generating partial results guaranteed to be in the skyline sets. We also propose an index structure for efficient organization of the cached queries that improve the efficiency. Experiments show the efficiency and scalability of our proposed methods.