About ACM Paris Kanellakis Theory and Practice Award

ACM has named Noga Alon of Princeton University and Tel Aviv University; Phillip Gibbons of Carnegie Mellon University; Yossi Matias of Google and Tel Aviv University; and Mario Szegedy of Rutgers University recipients of the ACM Paris Kanellakis Theory and Practice Award for seminal work on the foundations of streaming algorithms and their application to large-scale data analytics.

Alon, Gibbons, Matias and Szegedy pioneered a framework for algorithmic treatment of streaming massive datasets. Today, their sketching and streaming algorithms remain the core approach for streaming big data and constitute an entire subarea of the field of algorithms. Additionally, the concepts of sketches and synopses that they introduced are now routinely used in a variety of data analysis tasks in databases, network monitoring, usage analytics in internet products, natural language processing and machine learning.

In their seminal paper, “The Space Complexity of Approximating the Frequency Moments,” Alon, Matias and Szegedy laid the foundations of the analysis of data streams using limited memory. Follow-up papers, including “Tracking Join and Self-join Sizes in Limited Storage,” by Alon, Gibbons, Matias, and Szegedy, and “New Sampling-Based Summary Statistics for Improving Approximate Query Answers,” by Gibbons and Matias, expanded on the idea of data synopses and were instrumental in the development of the burgeoning fields of streaming and sketching algorithms. This work has been applied to query planning and processing in databases and the design of small synopses to monitor vast quantities of data generated in networks.