Not all data is equal and some require greater attention and priority than others. Industrial companies managing networks need to focus on information-based, mission critical and real-time data.
MIT researchers have developer a flash-storage system designed to cut the energy and physical space required to store and manage data in data centers by half.
Articles about low-power hybrid chips for robots, gaining the edge in automation, connecting quality and process data, manufacturing and process facility trends, and the Engineers' Choice Awards were Control Engineering’s five most clicked articles from March 18-24. Miss something? You can catch up here.
Using advanced analytics can help assure product quality and overall operational efficiencies. See case study example.
Demystify the need for Big Data and five related challenges: data structure, scalability, integration, storage, and upgrades.
Companies need to be open to new technologies to see fundamental paradigm shifts in productivity, beyond incremental improvements. Rapid increases in productivity, visibility, flexibility, and agility are needed.
Some applications require data acquisition strategies without cloud connectivity, but they can still benefit from advanced analytics technology.
Centralized data architectures are adapting to new opportunities for data collection and analytics.
TrendMiner 2019.R1 self-service analytics software release for process manufacturing includes a visual representation of time-series data, making it easier for users to analyze, monitor and predict process and asset performance.
FieldComm Group's process automation device information model specification (PA-DIM) has been endorsed by the OPC Foundation, and Profibus & Profinet International (PI).