The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Introduction: Recent Developments in Parallel and Distributed Data Mining
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
An Architecture for Distributed Enterprise Data Mining
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Distributed data mining on the grid
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Brain Meets Brawn: Why Grid and Agents Need Each Other
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
MAGE: An Agent-Oriented Programming Environment
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Grid-enabled data warehousing for molecular engineering
Parallel Computing - Special issue: High-performance parallel bio-computing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Using P2P, GRID and Agent technologies for the development of content distribution networks
Future Generation Computer Systems
A grid data mining architecture for learning classifier systems
WSEAS Transactions on Computers
Supervised learning classifier systems for grid data mining
CIS'09 Proceedings of the international conference on Computational and information science 2009
CDNsim: A simulation tool for content distribution networks
ACM Transactions on Modeling and Computer Simulation (TOMACS)
APHID: An architecture for private, high-performance integrated data mining
Future Generation Computer Systems
A resource-awareness information extraction architecture on mobile grid environment
Journal of Network and Computer Applications
GridclassTK: toolkit for grid learning classifier systems
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Grid data mining by means of learning classifier systems and distributed model induction
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Distributed data mining for e-business
Information Technology and Management
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Service oriented grid computing architecture for distributed learning classifier systems
MEDI'11 Proceedings of the First international conference on Model and data engineering
A multi-agent data mining system for cartel detection in Brazilian government procurement
Expert Systems with Applications: An International Journal
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Centralized data mining techniques are widely used today for the analysis of large corporate and scientific data stored in databases. However, industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed systems. The Grid can play a significant role in providing an effective computational infrastructure support for this kind of data mining. Similarly, the advent of multi-agent systems has brought us a new paradigm for the development of complex distributed applications. During the past decades, there have been several models and systems proposed to apply agent technology building distributed data mining (DDM). Through a combination of these two techniques, we investigated the critical issues to build DDM on Grid infrastructure and design an Agent Grid Intelligent Platform as a testbed. We also implement an integrated toolkit VAStudio for quickly developing agent-based DDM applications and compare its function with other systems.