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Efficient Management of Historic Graph Data and High-Volume Data Streams

Speaker: Jeong-Hyon Hwang
University at Albany

October 22, 2013 - 4:00 p.m. to 5:00 p.m.
Location: CII (Low) 3051
Hosted By: Dr. Sibel Adali (x8407)


We are living in an era of data deluge where massive amounts of data are being produced continuously around the world. Furthermore, users are seeking convenient and efficient ways of extracting useful information from these data in applications ranging from finance and marketing to national security, social science, and transportation. In this talk, I will present two systems that my research team has been developing for the applications mentioned above. The first system, called G*, efficiently stores and queries data that represent large evolving networks, such as social networks, transportation networks, and the World Wide Web. For a series of graphs representing a network at different points in time, G* stores commonalities among these graphs on servers in a deduplicated fashion. G* executes analytic queries on graphs using a network of operators that process data in parallel. To speed up queries on multiple graphs, these operators process commonalities among graphs only once and share the result across all relevant graphs. The second system, iFlow, processes high-volume data streams with low latency, enabling large scale monitoring applications. In iFlow, users express the desired computation as a network of operators. iFlow's off-the-shelf operators provide fundamental data processing capabilities such as filtering, aggregation, and integration of data. In addition, custom operators for specific applications can easily be developed and plugged into the system. Given a large number of servers around the world, iFlow strives to deploy operators in a manner that maximizes the performance/cost ratio. It also prevents service disruptions by replicating operators and rerouting around network outages.


Jeong-Hyon Hwang is an assistant professor in the Department of Computer Science at the University at Albany -- State University of New York. He received his Ph.D. in Computer Science from Brown University in 2008. Jeong-Hyon’s research interests include databases and distributed systems. He is a recipient of the NSF CAREER Award (2012), ACM SIGMOD Best Demonstration Award (2005), IBM Open Collaborative Faculty Award (2010), as well as a national scholarship and a software development award, both from the Ministry of Information and Communication of Korea.

Last updated: October 6, 2013