The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
A grid-oriented genetic algorithm framework for bioinformatics
New Generation Computing - Grid systems for life sciences
Materialization of fragmented views in multidimensional databases
Data & Knowledge Engineering
Fundamentals of Database Systems, Fourth Edition
Fundamentals of Database Systems, Fourth Edition
Hybrid greedy and genetic algorithms for optimization of relational data warehouses
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Framework for Workflow Gridication of Genetic Algorithms in Java
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
A genetic algorithm for the index selection problem
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
An evolutionary approach to schema partitioning selection in a data warehouse
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A grid-oriented genetic algorithm
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
Clustering-based materialized view selection in data warehouses
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
Materialized view selection as constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Horizontal partitioning by predicate abstraction and its application to data warehouse design
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Hi-index | 0.00 |
Generalized problem optimization of the relational data warehouses ,i.e., selection of the optimal set of views, their optimal fragmentation and their optimal set of indexes is very complex and still a challenging problem. Therefore, choice of optimization method and improvements of optimization process are essential. Our previous research was focused on utilization of Genetic Algorithms for problem optimization. In this paper we further optimize our solution by applying our novel Java Gid framework for Genetic Algorithms (GGA) in the process of relational data warehouses optimization. Obtained experimental results have shown, that for different input parameters, GGA dramatically improves efficiency of the optimization process.