Knowledge Analytics in Cloud Centric IoT Vicinities - Mishra, Nilamadhab; Alebachew, Kindie; Chandra Patnaik, Bikash (International Journal of Computer Sciences and Engineering, 2025)
The rapid increasing of real-time applications in today’s IoT (internet of things) world progressively lead to several problem issues such as, data volume, velocity, variety, and value. The study reveals that around 80% data in today’s IoT world are unstructured and needs an extensive knowledge exploration framework to turn the massively produced data into cognitive values (knowledge) of goldmines. In contemporary IoT vicinities, the time to get knowledge is very slow and the applicability of the knowledge is very poor, so the knowledge researchers start looking new framework that deals with the problems of semantic knowledge analytics and inference. In a typical semantic knowledge analytic scenario, context specification, rule specification, and frame specification may be used to define the structural relationships of knowledge, where the contexts, rules, and frames are stored as specification of framework and sub-framework. In this work, we investigate the viability of a context and rule Analytic framework (CORA-framework) for trustful knowledge analytic and inference in the cloud centric IoT vicinities. We also investigate a knowledge inference case in order to estimate the probabilistic error analysis based on outlier prospect and the knowledge analytic precision based on the root mean square error prospect. The analysis and discussion suggest implementing an outlier analytic mechanism to increase CORA-framework accuracy with estimating the error prediction outcome.