Semantics of Data Mining Services in Cloud Computing

Abstract

In recent years with the rise of Cloud Computing (CC), many companies providing services in the cloud, are empowering a new series of services to their catalogue, such as data mining (DM) and data processing (DP), taking advantage of the vast computing resources available to them. Different service definition proposals have been put forward to address the problem of describing services in CC in a comprehensive way. Bearing in mind that each provider has its own definition of the logic of its services, and specifically of DM services, it should be pointed out that the possibility of describing services in a flexible way between providers is fundamental in order to maintain the usability and portability of this type of CC services. The use of semantic technologies based on the proposal offered by Linked Data (LD) for the definition of services, allows the design and modelling of DM services, achieving a high degree of interoperability. In this article a schema for the definition of DM services on CC is presented considering all key aspects of service in CC, such as prices, interfaces, Software Level Agreement (SLA), instances or DM workflow, among others. The new schema is based on LD, and it reuses other schemata obtaining a better and more complete definition of the services. In order to validate the completeness of the scheme, a series of DM services have been created where a set of algorithms such as Random Forest (RF) or KMeans are modeled as services. In addition, a dataset has been generated including the definition of the services of several actual CC DM providers, confirming the effectiveness of the These services allow the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques on a large variety of data, offering an extensive catalogue of algorithms and workflows related to DM. Services, such as Amazon SageMaker 1 or Microsoft Azure Machine Learning Studio 2 (Table 1), offer a set of algorithms as services within CC platforms. Following this line, other CC platforms such as Algorithmia 3 or Google Cloud ML 4 , offer ML services at the highest level, providing specific services for the detection of objects in photographs and video, sentiment analysis, text mining or forecasting, for instance.Each CC service provider offers a narrow definition of these services, which is generally incompatible with other service providers. For instance, where one provider has a service for RF algorithm, another provider has another name, features, or parameters for that algorithm, although the two might be the same. This makes it difficult to define services or service models independent of the provider as well as to compare services through a CC service broker [4]. Indeed, a standardization of the definition of services would boost competitiveness, allowing third parties to operate with these services in a totally transparent way, skipping the individual details of the providers. The effectiveness of CC would be greatly improved if there were a general standard for services definition [5].There ar...

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