DSG Seminar Series • Algorithms and Optimizations for Incremental Window-Based Aggregations

Panos K. Chrysanthis, University of Pittsburgh

Abstract: Online analytics, in most advanced scientific, business, and defense applications, rely heavily on the efficient execution of large numbers of Aggregate Continuous Queries (ACQs). ACQs continuously aggregate streaming data and periodically produce results such as max or average over a given window of the latest data.  It was shown that in processing ACQs it is beneficial to use incremental evaluation, which involves storing and reusing calculations performed over the unchanged parts of the window, rather than performing the re-evaluation of the entire window after each update. 

In this talk, we examine how the principle of sharing is applied in the partial and final aggregation techniques and present our SlickDeque and WeaveShare techniques that optimize the execution of multi-ACQs in single and multiple computing nodes.

Bio: Panos K. Chrysanthis is a Professor of Computer Science and the founding director of the Advanced Data Management Technologies Laboratory in the School of Computing and Information at the University of Pittsburgh.  He is also an Adjunct Professor at the Carnegie Mellon University and University of Cyprus.

His research interests lie at the intersection of data management, distributed systems and collaborative applications. He is a recipient of the NSF CAREER Award and he is an ACM Distinguished Scientist and a Senior Member of IEEE. He is also a recipient of the University of Pittsburgh Provost Award for Excellence in Mentoring (doctoral students). He is currently the Special Issues Coordinator for the Distributed and Parallel Databases Journal and a Program Committee Co-chair of IEEE ICDE 2018. He earned his BS degree from the University of Athens, Greece  and his MS and PhD degrees from the University of Massachusetts at Amherst.