Review

The Impact of Data Mining and SaaS-Cloud Computing: A Review

Volume: 3 Number: 1 June 24, 2020
Abir Achache *, Abdelhalim Baaziz , Toufik Sari

The Impact of Data Mining and SaaS-Cloud Computing: A Review

Abstract

Cloud Computing has emerged as a powerful paradigm that has successfully dominated network services in many application areas and significantly transformed the IT industry. In the Cloud Computing, all resources are available as services and accessible via the Internet. Software as a Serviceis considered as the king of service delivery models that enable end-users to access to any software or application as a service via the Internet, without local installation. Over the past decade, this model has been widely adopted by many organizations and individuals, leading to the production and accumulation of a huge amount of data stored in the Cloud from distributed nodes that must be recovered very efficiently. Software as a Service providers must be able to manage this data successfully, evaluate and improve the quality of their solutions in order to provide reliable and efficient services to users of Cloud services. On the other hand, Data Mining is a current trend in the field of data treatment that allows the extraction of useful and meaningful information from raw data. The integration of Data Mining techniques into Cloud Computing –Software as a Service -has become commonplace and can support the adequate provision of services by providing agility and rapid access to technology. This article introduces the basic concepts of Data Mining and Cloud Computing first, and discusses the coupling of the two paradigms. Then, it describes how Data Mining can be used and integrated to improve Software as a Service services in the Cloud. Finally, it reviews relevant and important research in this area.

Keywords

Classification , Clustering , Cloud Computing , Data Mining , KDD , Software as a Service

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IEEE
[1]A. Achache, A. Baaziz, and T. Sari, “The Impact of Data Mining and SaaS-Cloud Computing: A Review”, DataSCI, vol. 3, no. 1, pp. 23–35, June 2020, [Online]. Available: https://izlik.org/JA34NB33CD