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Category: MTech Cloud Computing Projects
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MTech Projects
02.May
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Semantics of Cloud Computing Data Mining Services

PROJECT TITLE :

Semantics of Data Mining Services in Cloud Computing

ABSTRACT:

Users now have access to extremely comprehensive data analysis tools that include all of the benefits that come along with working in an environment of this kind as a result of the recent incorporation of new Data Mining and Machine Learning services within Cloud Computing providers. The descriptions and definitions of cloud computing for data mining are published by the various service providers in a wide variety of formats, many of which are incompatible with those used by other service providers. To maintain the usability and especially the portability of these services regardless of the software/hardware support or even the differences between cloud platforms, it is essential to have the capability to describe complete Data Mining services. This is true from a functional standpoint. The primary objective of this article is to design a Data Mining service definition that enables the composition of an entire service using a single, straightforward definition. This will make it possible for a data mining workflow to be ported and deployed in the services of multiple providers or even in a Market Place for ready-to-consume services of this type. This article presents a semantic scheme for the definition and description of complete Data Mining services. This scheme takes into consideration both the management of the service by the provider (price, authentication, Service Level Agreement, etc.) as well as the definition of the Data Mining workflow as a service. It is an important step toward the eventual standardization and industrialization of data mining services and represents a significant contribution to this process. For the purpose of determining whether or not the plan is sound, a description has been given of a selection of services offered by Data Mining service providers, as well as a definition of what constitutes a full service for a Random Forest algorithm. In addition, a practical scenario has been developed, which involves the creation of a deployment platform for Data Mining services in order to give functional support to the scheme. This helps to illustrate the practical benefits that the proposal has in store for the end user.

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