An Approach Prioritizing The Causal Factors Of Large Scaled Data Using Soft Computing: A Case Study

In general situation, the high intensive tasks linked to computation can be provisioned either through dedicated servers or can be properly filtered in virtual platforms. The major constraint in such situation can be associated with obtaining decision in process initiated as well as in the cost of data transmission preserving security. Sometimes some specific issues are required to be resolved during utilization of Internet of Things in specified applications expecting feasible solutions. Often it has been observed that the traditional computing mechanisms linked with the devices like routers equipped with specific infrastructures as well as services may not be adequate for implementation due to lack of flexibilities. In such situation, it may be difficult for data acquisition and processing. In fact, this complexity can be due to constrain in operations linked to computational resources especially in distributed environments. Sometimes also it is required to focus on specific data retrieved from different IoT distributed components linked to virtual machines. Accordingly, the techniques should be enabled on proper accumulation of data with accurate prediction prioritizing the causal factors and data sharing mechanisms. Though it is equally important to handle large scaled data related to issues of multi domain applications, it is essential to enhance the modularity, flexibility as well as scalability of the data and to maintain the optimal accuracy. Also to address these issues, specific computational approaches especially ant colony optimization technique can be the support to make commonalities and obtain close association of the resources with the relevant data. The implementation mechanism in virtual machines also supports integration of complex data and provisions privacy with security.