Designing data analysis services in the Knowledge Grid

Grid environments were originally designed for dealing with problems involving compute-intensive applications. Today, however, grids enlarged their horizon as they are going to manage large amounts of data and run business applications supporting consumers and end users. To face these new challenges, grids must support adaptive data management and data analysis applications by offering resources, services, and decentralized data access mechanisms. In particular, according to the service-oriented architecture model, data mining tasks and knowledge discovery processes can be delivered as services in grid-based infrastructures. By means of a service-based approach it is possible to define integrated services supporting distributed business intelligence tasks in grids. These services can address all the aspects involved in data mining and knowledge discovery processes: from data selection and transport, to data analysis, knowledge models representation and visualization. We worked along this direction for providing a gridbased architecture supporting distributed knowledge discovery named Knowledge Grid. This report discusses how the Knowledge Grid framework has been developed as a collection of grid services and how it can be used to develop distributed data analysis tasks and knowledge discovery processes exploiting the service-oriented architecture model.