Guest Editors' Introduction: Distributed Data Mining--Framework and Implementations
IEEE Internet Computing
Distributed data mining services leveraging WSRF
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Distributed data mining in grid computing environments
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Service-oriented middleware for distributed data mining on the grid
Journal of Parallel and Distributed Computing
Middleware for data mining applications on clusters and grids
Journal of Parallel and Distributed Computing
FREERIDE-G: enabling distributed processing of large datasets
DADC '08 Proceedings of the 2008 international workshop on Data-aware distributed computing
Parallel fuzzy c-means cluster analysis
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
Lightweight clustering technique for distributed data mining applications
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Combination methodologies of multi-agent hyper surface classifiers: design and implementation issues
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Performance-based data distribution for data mining applications on grid computing environments
The Journal of Supercomputing
Performance study of distributed Apriori-like frequent itemsets mining
Knowledge and Information Systems
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
Service oriented grid computing architecture for distributed learning classifier systems
MEDI'11 Proceedings of the First international conference on Model and data engineering
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
A heterogeneous computing system for data mining workflows
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Distributed process discovery and conformance checking
FASE'12 Proceedings of the 15th international conference on Fundamental Approaches to Software Engineering
Decomposing process mining problems using passages
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
Distributed data mining patterns and services: an architecture and experiments
Concurrency and Computation: Practice & Experience
Decomposing Petri nets for process mining: A generic approach
Distributed and Parallel Databases
Process Discovery and Conformance Checking Using Passages
Fundamenta Informaticae - Application and Theory of Petri Nets and Concurrency, 2012
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Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called KNOWLEDGE GRID. This paper describes the KNOWLEDGE GRID framework and presents the toolset provided by the KNOWLEDGE GRID for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the KNOWLEDGE GRID tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.