Vertical partitioning algorithms for database design
ACM Transactions on Database Systems (TODS)
Data allocation in distributed database systems
ACM Transactions on Database Systems (TODS)
Information Sciences—Informatics and Computer Science: An International Journal
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
How to solve it: modern heuristics
How to solve it: modern heuristics
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Allocating Data and Operations to Nodes in Distributed Database Design
IEEE Transactions on Knowledge and Data Engineering
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Efficient Progressive Sampling for Association Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Distribution Design of Logical Database Schemas
IEEE Transactions on Software Engineering
The farthest point strategy for progressive image sampling
IEEE Transactions on Image Processing
Hi-index | 0.00 |
In this paper we deal with the solution of very large instances of the design distribution problem for distributed databases. Traditionally the capacity for solving large scale instances of NP-hard problems has been limited by the available computing resources and the efficiency of the solution algorithms. In contrast, in this paper we present a new solution approach that permits to solve larger instances using the same resources. This approach consists of the application of a systematic method for transforming an instance A into a smaller instance A' that has a large representativeness of instance A. For validating our approach we used a mathematical model developed by us, whose solution yields the design of a distributed database that minimizes its communication costs. The tests showed that the solution quality of the transformed instances was on the average 10.51% worse than the optimal solution; however, the size reduction was 97.81% on the average. We consider that the principles used in the proposed approach can be applied to the solution of very large instances of NP-hard problems of other problem types.