Optimized Continuous Quality and Storage Management Model for Big Data Analysis

Due to the increase in devices and the availability of connecting devices, data creation increased gradually. To handle the huge amount of data from many different sources, the business analysis had to use effective analysis and storage optimization. So, one of the main areas of research to handle big data, keep its quality up, and get the most out of storage is how to analyze and store big data. This paper introduced a new framework for collaborative big data analysis and optimized storage using the new framework model. The proposed framework model is D2SAE (Dynamic Domain Sample Attributes Evaluation). The proposed framework comprises four main steps: dynamic data collection from domains, sample attribute collection, evaluation metrics for quality data analytics, and an optimization process. The proposed framework was evaluated using metrics such as throughput, runtime, average latency, minimum latency, and maximum latency. Our proposed model produces better throughput and reduces runtime and latency compared to the previous framework. The results show that the throughput increases and the run time is reduced to 42 seconds.