Presented By O'Reilly and Cloudera
Make Data Work
Feb 17–20, 2015 • San Jose, CA
Robert Grossman

Robert Grossman
Director, Center for Data Intensive Science, University of Chicago

Website

Robert Grossman is a faculty member and the Chief Research Informatics Officer in the Biological Sciences Division of the University of Chicago. He is the Director of the Center for Data Intensive Science and a Senior Fellow in the Computation Institute (CI) and the Institute for Genomics and Systems Biology (IGSB). He is also the Founder and a Partner of Open Data Group, which specializes in building predictive models over big data. He has led the development of open source software tools for analyzing big data (Augustus), distributed computing (Sector), and high performance networking (UDT). In 1996 he founded Magnify, Inc., which provides data mining solutions to the insurance industry and was sold to ChoicePoint in 2005. He is also the Chair of the Open Cloud Consortium, which is a not-for-profit that supports the research community by operating cloud infrastructure, such as the Open Science Data Cloud. He blogs occasionally about big data, data science, and data engineering at rgrossman.com.

Sessions

1:30pm–2:10pm Friday, 02/20/2015
Practical Methods for Identifying Anomalies That Matter in Large Datasets
Machine Data / IoT
 Location: LL21 E/F
Robert Grossman (University of Chicago)
Average rating: ****.
(4.60, 15 ratings)
Finding anomalies is essential for a wide range of applications, including cybersecurity, event detection and health and status monitoring. Anomaly techniques that scale successfully to large datasets tend to integrate machine learning with good data engineering. We discuss three case studies and extract eight techniques that have proved effective for detecting anomalies in large scale systems. Read more.