Introduction to Linear Optimization
Introduction to Linear Optimization
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
Algorithms for Querying by Spatial Structure
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
Constructing and exploring composite items
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Top-k combinatorial skyline queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
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Multi-objective optimization problem finds out optimal objects w.r.t. several objectives rather than a single objective. We propose a new problem called a multi-objective optimal combination problem (MOC problem) which finds out object combinations w.r.t. multiple objectives. A combination dominates another combination if it is not worse than anther one in all attributes and better than another one in one attribute at least. The combinations, which cannot be dominated by any other combinations, are optimal. We propose an efficient algorithm to find out optimal combinations by reducing the search space with a lower bound reduction method and an upper bound reduction method based on the R-tree index. We implemented the proposed algorithm and conducted experiments on synthetic data sets.