The 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.