Discrete and Combinatorial Mathematics: An Applied Introduction
Discrete and Combinatorial Mathematics: An Applied Introduction
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Mining thick skylines over large databases
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
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
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
SUBSKY: Efficient Computation of Skylines in Subspaces
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Refreshing the sky: the compressed skycube with efficient support for frequent updates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Answering top-k queries using views
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient skyline computation over low-cardinality domains
VLDB '07 Proceedings of the 33rd 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
Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient skyline querying with variable user preferences on nominal attributes
Proceedings of the VLDB Endowment
Online Skyline Analysis with Dynamic Preferences on Nominal Attributes
IEEE Transactions on Knowledge and Data Engineering
Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Efficient Processing of Metric Skyline Queries
IEEE Transactions on Knowledge and Data Engineering
Top-k combinatorial skyline queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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Current skyline evaluation techniques are mainly to find the outstanding tuples from a large dataset. In this paper, we generalize the concept of skyline query and introduce a novel type of query, the combinatorial skyline query, which is to find the outstanding combinations from all combinations of the given tuples. The past skyline query is a special case of the combinatorial skyline query. This generalized concept is semantically more abundant when used in decision making, market analysis, business planning, and quantitative economics research. In this paper, we first introduce the concept of the combinatorial skyline query (CSQ) and explain the difficulty in resolving this type of query. Then, we propose two algorithms to solve the problem. The experiments manifest the effectiveness and efficiency of the proposed algorithms.