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005			20250424114126.0
008			250424b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925977 _aVenkataramana, Jaladurgam
245			_aLeveraging data-driven techniques for efficient data mining in cloud computing environments
250			_aVol.15(2), Oct
260			_aChennai _bICT Academy _c2024
300			_a3515-3522p.
520			_aThe capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.
650		0	_94622 _aComputer Engineering
773	0		_dChennai ICT Academy _tICTACT Journal on Soft Computing (IJSC)
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_6_3515_3522.pdf _yClick here
942			_2ddc _cAR