Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Communications of the ACM
GATES: A Grid-Based Middleware for Processing Distributed Data Streams
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
A Services Oriented Framework for Next Generation Data Analysis Centers
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 10 - Volume 11
The Design of Discovery Net: Towards Open Grid Services for Knowledge Discovery
International Journal of High Performance Computing Applications
Distributed data mining services leveraging WSRF
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Semantics and Knowledge Grids: Building the Next-Generation Grid
IEEE Intelligent Systems
The Weka4WS framework for distributed data mining in service-oriented Grids
Concurrency and Computation: Practice & Experience
Digging Deep into the Data Mine with DataMiningGrid
IEEE Internet Computing
On agents and grids: Creating the fabric for a new generation of distributed intelligent systems
Web Semantics: Science, Services and Agents on the World Wide Web
An overview of S-OGSA: A Reference Semantic Grid Architecture
Web Semantics: Science, Services and Agents on the World Wide Web
Globus toolkit version 4: software for service-oriented systems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Social Services Computing: Concepts, Research Challenges, and Directions
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Distributed data mining patterns and services: an architecture and experiments
Concurrency and Computation: Practice & Experience
International Journal of Healthcare Information Systems and Informatics
Cloud4SNP: Distributed Analysis of SNP Microarray Data on the Cloud
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
SMINER - a platform for data mining based on service-oriented architecture
International Journal of Business Intelligence and Data Mining
A tensor-based distributed discovery of missing association rules on the cloud
Future Generation Computer Systems
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Introduction Computer science applications are becoming more and more network centric, ubiquitous, knowledge intensive, and computing demanding. This trend will result soon in an ecosystem of pervasive applications and services that professionals and end-users can exploit everywhere. Recently, collections of IT services and applications, such as Web services and Cloud computing services, became available opening the way for accessing computing services as public utilities, like water, gas and electricity. Key technologies for implementing that perspective are Cloud computing and Web services, semantic Web and ontologies, pervasive computing, P2P systems, Grid computing, ambient intelligence architectures, data mining and knowledge discovery tools, Web 2.0 facilities, mashup tools, and decentralized programming models. In fact, it is mandatory to develop solutions that integrate some or many of those technologies to provide future knowledge-intensive software utilities. The Grid paradigm can represent a key component of the future Internet, a cyber infrastructure for efficiently supporting that scenario. Grid and Cloud computing are evolved models of distributed computing and parallel processing technologies. The Grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. In the area of Grid computing a proposed approach in accordance with the trend outlined above is the Service-Oriented Knowledge Utilities (SOKU) model that envisions the integrated use of a set of technologies that are considered as a solution to information, knowledge and communication needs of many knowledge-based industrial and business applications. The SOKU approach stems from the necessity of providing knowledge and processing capabilities to everybody, thus supporting the advent of a competitive knowledge-based economy. Although the SOKU model is not yet implemented, Grids are increasingly equipped with data management tools, semantic technologies, complex work-flows, data mining features and other Web intelligence approaches. Similar efforts are currently devoted to develop knowledge and intelligent Clouds. These technologies can facilitate the process of having Grids and Clouds as strategic components for supporting pervasive knowledge intensive applications and utilities. Grids were originally designed for dealing with problems involving large amounts of data and/or compute-intensive applications. Today, however, Grids enlarged their horizon as they are going to run business applications supporting consumers and end-users. To face those new challenges, Grid environments must support adaptive knowledge management and data analysis applications by offering resources, services, and decentralized data access mechanisms. In particular, according to the service-oriented architecture (SOA) model, data mining tasks and knowledge discovery processes can be delivered as services in Grid-based infrastructures. Through a service-based approach we can define integrated services for supporting distributed business intelligence tasks in Grids. Those services can address all the aspects that must be considered in data mining and in knowledge discovery processes such as data selection and transport, data analysis, knowledge models representation and visualization. We worked in this direction for providing Grid-based architectures and services for distributed knowledge discovery such as the Knowledge Grid the Weka4WS toolkit, and mobile Grid services for data mining. Here we describe a strategy and a model based on the use of services for the design of distributed knowledge discovery services and discuss how Grid frameworks, such those mentioned above, can be developed as a collection of services and how they can be used to develop distributed data analysis tasks and knowledge discovery processes using the SOA model.