Scalable Distributed Change Detection from Astronomy Data Streams Using Local, Asynchronous Eigen Monitoring Algorithms.

This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be coanalyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We ...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Kamalika Das, Kanishka Bhaduri, Sugandha Arora, Wesley Griffin, Kirk D. Borne, Chris Giannella, Hillol Kargupta
• Keywords: Computer Science, Data Mining Tasks, Distributed Asynchronous Algorithm, Dynamic Data Streams, SDM 2009