<![CDATA[The massively generated data from various technologically advanced applications hosted in the cloud and internet of things (IoT) in present times calls for effective management towards balancing the demands of both service providers and users. The conventional usage of distributed frameworks for such big data management is witnessed with various ongoing challenges. Hence, this manuscript presents a novel analytical framework for big data that can offer reduced cost and reduced time demanded to evaluate the distributed big data from multiple data points in the cloud in an optimal way. The core ideology of this framework is to gain a synchronized optimality for cost and time for executing a task demanded for big data analytics complying with the constraints associated with task deadline. The proposed framework is capable of fine-tuning the positioning of task operation using transform and aggregate strategy to exhibit 37% reduced delay, 41% efficient task completion performance, and 28% reduced execution time in contrast to existing frameworks.]]>
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