A Cognitive Oriented Framework for IoT Big-data Management Prospective
Nilamadhab Mishra, Chung‐Chih Lin, Hsien-Tsung Chang · 2014
The Internet-of-Things (IoT) environment continuously inspires any time-place-things connectivity of the smart objects in and around the universe. Day by day the rapid growth of enormous IoT objects and digital storage technology, lead to a large heterogeneous data depository, in which the IoT big-data are stored in the dissimilar database frameworks as a consequence of heterogeneous data sources. So due to large heterogeneous data sources, some incompatibilities like name, scale, structure, and level of abstraction are there in between the frameworks of IoT big-data that create threats to data management and knowledge discovery. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and a data organization framework for effective data management and knowledge discovery that cop-up with the large scale industrial automation applications. The discussion and analysis shows that the proposed framework and architectures creates a feasible solution in implementing IoT big-data based smart industrial applications.