A Computation of Time Management System in FTC System and Creating Strategy

Using a use-case approach, we go into the specific ontology development, adding semantic rules to enhance its formulation and resolution. Our methodology seeks to capture the heterogeneity, variety, and complexity of data, as data itself provides these attributes. This explains why the different Big Data analytics difficulties are adaptable and scalable in a wide range of scenarios. By implementing this approach, we pave the way for the healthcare sector and beyond to use big data more effectively, improving patient outcomes through informed decision-making. This paper introduces a novel method for solving these problems: dynamics-based integer linear programming. By effectively allocating resources from dark computing to IoT devices, this method seeks to optimise workflow. It also aims to reduce stress associated with limited stealth resource containment by accelerating time-sensitive workflows and solving the termination problem as an integer linear programming (ILP) problem. Suggested With its task-first structure, the theory lowers fog node power consumption and delays in the environment by taking into account dynamic activities of greater quality. The trials’ findings demonstrate that, in terms of energy usage and service latency, the suggested strategy performs better than the experimental approaches.