Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA
Michael Freedman

Michael Freedman
Cofounder and CTO | Professor of Computer Science, TimescaleDB | Princeton

Website

Michael J. Freedman is a professor in the Computer Science Department at Princeton University and the cofounder and CTO of TimescaleDB, which provides an open source time series database optimized for fast ingest and complex queries. His research broadly focuses on distributed systems, networking, and security. He developed and operates several self-managing systems, including CoralCDN (a decentralized content distribution network) and DONAR (a server resolution system that powered the FCC’s Consumer Broadband Test), both of which serve millions of users daily. Michael’s other research has included software-defined and service-centric networking, cloud storage and data management, untrusted cloud services, fault-tolerant distributed systems, virtual world systems, peer-to-peer systems, and various privacy-enhancing and anticensorship systems. Michael’s work on IP geolocation and intelligence led him to cofound Illuminics Systems, which was acquired by Quova (now part of Neustar). His work on programmable enterprise networking (Ethane) helped form the basis for the OpenFlow/software-defined networking (SDN) architecture. His honors include the Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan fellowship, the NSF CAREER Award, the Office of Naval Research Young Investigator Award, DARPA Computer Science Study Group membership, and multiple award publications. Michael holds a PhD in computer science from NYU’s Courant Institute and both an SB and an MEng degree from MIT.

Sessions

5:10pm5:50pm Wednesday, March 15, 2017
Real-time applications
Location: LL20 D Level: Intermediate
Secondary topics:  IoT, Streaming
Michael Freedman (TimescaleDB | Princeton)
Average rating: *****
(5.00, 3 ratings)
IoT applications often need more-complex queries than those supported by traditional time series databases. Michael Freedman outlines a new distributed time series database for such workloads, supporting efficient queries, including complex predicates across many metrics, while scaling out to support IoT ingest rates. Read more.
11:00am11:40am Thursday, March 16, 2017
Location: Table A
Michael Freedman (TimescaleDB | Princeton)
Stop by and talk to Michael if you have questions about time series data or the new distributed time series database TimescaleDB. Read more.