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Data Mining and Knowledge Discovery Handbook, 2 Edition part 105

Data Mining and Knowledge Discovery Handbook, 2 Edition part 105. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 1020 Antonio Congiusta Domenico Talia and Paolo Trunfio whose main goal is to design develop and implement an infrastructure to effectively support scientific knowledge discovery processes from high-throughput informatics. In this context a series of testbeds and demonstrations are being carried out for using the technology in the areas of life sciences environmental modeling and geo-hazard prediction. The building blocks in Discovery Net are the so-called Knowledge Discovery Services KDS distinguished in Computation Services and Data Services. The former typically comprise algorithms e.g. data preparation and Data Mining while the latter define relational tables as queries and other data sources. Both kinds of services are described and registered by means of Adapters providing information such as input and output types parameters location and or platform operating system constraints factories objects allowing to retrieve references to services and to download them keywords and a human-readable description. KDS are used to compose moderately complex data-pipelined processes. The composition may be carried out by means of a GUI which provides access to a library of services. The XML-based language used to describe processes is called Discovery Process Markup Language DPML . Each composed process can be deployed and published as a new process. Typically process descriptions are not bound to specific servers since the actual resources are later resolved by lookup servers see below . Discovery Net is based on an open architecture using common protocols and infrastructures such as Globus Toolkit. Servers are distinguished into i Knowledge Servers allowing storage and retrieval of knowledge meant as raw data and knowledge models and processes ii Resource Discovery Servers providing a knowledge base of service definitions and performing resource resolution iii Discovery Meta-Information Servers used to store information about the Knowledge Schema i.e. the sets of .