How a Physics-Driven Analytics Platform Detects Reliability Threats
Session date: Feb 26, 2020 09:00am (Pacific Time)
A physics-driven analytics platform aids in improvements to the reliability and efficiency of connected mechanical systems. The solution analyzes large quantities of time series data from IoT sensors to help identify issues affecting system performance in real-time as well as provide accurate data for predictive maintenance. Our presenter chose a time series database for its high ingest and storage of time series data as well as its ability to easily send this data into their systems for predictive analytics.
During this latest Data Science Central webinar in association with IoTCentral, learn how using a purpose-built time series database helps to continuously optimize reliability of their customers’ connected mechanical systems.
Jon Herlocker
President and CEO, Tignis
Jon is a deep technologist and experienced executive in both on-premises enterprise software and consumer SaaS businesses. In his prior leadership roles, he was Vice President and CTO of VMware's Cloud Management Business Unit, which generated $1.2B/year for VMware. Other positions include CTO of Mozy, and CTO of EMC's Cloud Services division. As a co-founder of Tignis, Jon is an experienced entrepreneur, having founded two other startup companies. He sold his last startup, Smart Desktop, to Pi Corporation in 2006. Jon is a former tenured professor of Computer Science at Oregon State University, and his highly-cited academic research work was awarded the prestigious 2010 ACM Software System Award for contributions to the field of recommendation systems. Jon holds a Ph.D. in Computer Science from the University of Minnesota, and a B.S. in Mathematics and Computer Science from Lewis and Clark College.
