The Berkeley DB group holds a weekly lunch lecture series in the fall semester. This week’s speaker is Sailesh Krishnamurthy, one of the co-founders of Truviso (and a 2006 alum of the Berkeley DB group). I had the good fortune to work with Sailesh at Truviso, so I’m sure this will be a terrific talk. The abstract is below—this work on intelligently handling out-of-order data, and generalizing that to “corrections” of all kinds—was only beginning when I was finishing up at Truviso, but it seems really interesting.
Time: 1-2PM, November 6, 2009 (lunch starts at 12:30, the talk starts at 1)
Location: 606 Soda Hall, UC Berkeley
Title: ACDC – Analytics over Continuous and DisContinuous Streams
Streaming continuous analytics systems have emerged as key solutions for dealing with massive data volumes and demands for low latency. These systems have been heavily influenced by an assumption that data streams can be viewed as sequences of ordered data. The reality, however, is that streams are not continuous and disruptions of various sorts in the form of either big chunks of late arriving data or arbitrary failures are endemic. We argue, therefore, that stream processing needs a fundamental rethink and advocate a unified approach providing Analytics over Continuous and DisContinuous (ACDC) streams of data. Our approach is based on a simple insight – partially process independent runs of data and defer the consolidation of the associated partial results to when the results are actually used on an on demand basis. Not only does our approach provide the first real solution to the problem of data that arrives arbitrarily late, it also lets us solve a host of hard problems such as parallelism, recovery, transactional consistency and high availability that have been neglected by streaming systems. In this talk we describe the Truviso ACDC approach and outline some of the key technical arguments and insights behind it.
Speaker: Sailesh Krishnamurthy, Vice President of Technology and Founder, Truviso, Inc.
Dr. Sailesh Krishnamurthy, PhD is responsible for setting and driving the overall technical strategy and direction for the Truviso product and solution portfolio. In addition, he works in close collaboration with marketing, sales and engineering teams in managing the product and solution roadmap, performance engineering, and technology evangelism. Previously, he built and managed the initial engineering, services and support teams at Truviso. Sailesh is a leading authority in the field of enterprise data management with over a dozen published academic papers and several U.S. patents. Sailesh investigated the technical ideas at the heart of Truviso’s products as part of his doctoral research on stream query processing, earning a PhD. in Computer Science from UC Berkeley in 2006. Prior to graduate work at Berkeley, he worked at the Database Technology Institute at IBM Corporation where he designed and developed advanced features in IBM database products. Earlier, he worked on a Java virtual machine implementation at Netscape Communications. Sailesh has a Master’s degree in computer Science from Purdue University and a Bachelor’s degree in Electrical Engineering from the Birla Institute of Technology and Science in Pilani, India.
Thanks to Daisy Zhe Wang for organizing the DB seminar this semester.