Gaining insights by losing bits

Inventors of the year 2020 | Category Open Innovation

Data Compression for Industries

Digital factories produce enormous amounts of data. Their devices are part of the Industrial IoT. Some of the sensors produce ten thousand samples every second. This leads to ever growing costs for data storage and challenges in data transmission. In an Open Innovation project, Siemens and Stanford University partnered and created a new algorithm that leverages the redundancies of big data to substantially compress it. It has the potential to reduce the cost for bandwidth and storage of IoT data from industrial processes by up to 30 percent.
My team specializes in bridging the gap between industry and academia. Our goal is to create intelligent autonomous industrial machines with a combination of automation, digitalization and AI. Together with Stanford university we created an algorithm that decreases significantly the cost for bandwidth and storage of data from industrial processes and improves the environmental footprint.
Juan Aparicio Ojea, head of the Advanced Manufacturing Automation research group at Siemens Technology in Berkeley, California. 

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