Biblio

The cutting edge of Industry 4.0 has driven everything to be converted to disruptive innovation and digitalized. This digital revolution is imprinted by modern and advanced technology that takes advantage of Big Data and Artificial Intelligence (AI) to nurture from automatic learning systems, smart city, smart energy, smart factory to the edge computing technology, and so on. To harness an appealing, noteworthy, and leading development in smart manufacturing industry, the modern industrial sciences and technologies such as Big Data, Artificial Intelligence, Internet of things, and Edge Computing have to be integrated cooperatively. Accordingly, a suggestion on the integration is presented in this paper. This proposed paper describes the design and implementation of big data platform for intelligence industrial internet of things sensor monitoring system and conveys a prediction of any upcoming errors beforehand. The architecture design is based on edge computing and artificial intelligence. To extend more precisely, industrial internet of things sensor here is about the condition monitoring sensor data - vibration, temperature, related humidity, and barometric pressure inside facility manufacturing factory.

Real-time discovery of all different types of unique attributes within unstructured data is a challenging problem to solve when dealing with multiple petabytes of unstructured data volume everyday. Popular discovery solutions such as the creation of offline jobs to uniquely identify attributes or running aggregation queries on raw data sets limits real time discovery use-cases and often results into poor resource utilization. The discovery information must be treated as a parallel problem to just storing raw data sets efficiently onto back-end big data systems. Solving the discovery problem by creating a parallel discovery data store infrastructure has multiple benefits as it allows such to channel the actual search queries against the raw data set in much more funneled manner instead of being widespread across the entire data sets. Such focused search queries and data separation are far more performant and requires less compute and memory footprint.