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Data Mining in Grid Computing Environments by Werner Dubitzky

By Werner Dubitzky

Dependent round 11 overseas actual lifestyles case reviews and together with contributions from best specialists within the box this groundbreaking booklet explores the necessity for the grid-enabling of information mining functions and offers a entire research of the expertise, thoughts and administration talents essential to create them. This ebook offers a simultaneous layout blueprint, consumer advisor, and study schedule for present and destiny advancements and may entice a wide viewers; from builders and clients of information mining and grid know-how, to complex undergraduate and postgraduate scholars attracted to this box.

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Chapter 3 is entitled ‘GridMiner: an advanced support for e-science analytics’. It describes the architecture of the GridMiner system, which is based on the Cross Industry Standard Process for Data Mining. GridMiner provides a robust and reliable high-performance data mining and OLAP environment, and the system highlights the importance of grid-enabled applications in terms of e-science and detailed analysis of very large scientific data sets. Chapter 4 is entitled ‘ADaM services: scientific data mining in the service-oriented architecture paradigm’.

A client application that wants to submit a knowledge discovery computation to the Knowledge Grid does not need to interact with all of these services, but just with a few of them. Inside each group, in fact, there are two layers of services: high-level services and core-level services. The design idea is that user-level applications directly interact with high-level services, which, in order to perform client requests, invoke suitable operations exported by the core-level services. 2 The Knowledge Grid architecture core-level services perform their operations by invoking basic services provided by available grid environments running on the specific host, as well as by interacting with other core-level services.

3 Knowledge Grid services The Knowledge Grid is a software framework for implementing knowledge discovery tasks in a wide range of high-performance distributed applications. It offers to users high-level abstractions and a set of services by which they can integrate grid resources to support all the phases of the knowledge discovery process. The Knowledge Grid architecture is designed according to the SOA, which is a model for building flexible, modular and interoperable software applications. The key aspect of SOA is the concept of a service, a software block capable of performing a given task or business function.

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