Adaptive Multi-Resolution Framework for Scalable and Efficient Big Data Processing

The increasing applicability of the big data in different domains require the formulation of effective data collection and analysis framework that can accommodate complex and large datasets. This paper proposes the new DRAF that enhance both data aggregation and mining for large datasets. The Dynamic Resolution Adaptive Framework (DRAF) takes advantage of using multiple levels of representation of data, which makes it easy to expand the workload or scale down depending on the needs of the user. This proposed framework employs methods like hierarchical clustering, branching multi-level indexing and Adaptive Resolution Switching in order to optimize the trade-off of data accuracy and computational cost. Moreover, high level of detail is maintained in the DRAF, which supports mining necessary data and model details at multiple resolutions, making the DRAF specifically appropriate for operational environments where data come from numerous different sources and types. Substantial experiments in the book prove the efficiency of the DRAF-based algorithms in terms of speed and accuracy in comparison with other existing methods. The study establishes potentiality of the DRAF toward the application of big data problems in areas of high level of resolution and immediacy of decision making.