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Hadoop Talk: Details of Anomaly Detection in Big Data
July 28, 2014 @ 6:45 pm
Nikunj Oza, Leader, Data Sciences Group, NASA Ames Research Center
Speaker supplied by the Hadoop Talks meetup. See this link for the Hadoop Talks meetup group: http://www.meetup.com/Hadoop-Talks/
Agenda
*** Bring ID (e.g. Driver’s License) for eBay Security ***
6:30 Doors Open, Food & Networking
7:00 Presentation
*** Please arrive by 7 PM due to Security ***
Event Details
Data-driven methods for anomaly detection identifies as anomalies those data points that do not fit with most of the data in some sense. For example, the anomalies may have greater distances to their nearest neighbors or lower probabilities with respect to an appropriate probability model. However, measuring distances between points or probabilities of points is problematic when working with “big data,” with their heterogeneity and volume. In this talk, I will describe the problem in more detail, the heterogeneous data sources available to us, the methods we use to leverage these data sources, and the general data management and data mining problems that we need to solve moving forward.
Speaker Bio
Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads the Discovery of Precursors to Safety Incidents (DPSI) team which applies data mining to aviation safety. Dr. Oza’s 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His DPSI team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administrator’s Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Event page provided by ACM
https://www.sfbayacm.org/event/hadoop-talk-details-anomaly-detection-big-data