USA - 2019
For foundational work on streaming algorithms and their application to large scale data analytics
Noga Alon, Phillip Gibbons, Yossi Matias and Mario Szegedy pioneered a framework of algorithmic techniques for analyzing streaming data sets. Massive data is processed while retaining only small randomized "synopses", or "sketches", that require limited memory and provide approximate answers. In a groundbreaking line of research work they introduced techniques for several key problems including estimation of frequency moments.
Due to its simplicity and elegance, their work has been instrumental in the development of the field of streaming algorithms, which is one of the most prolific and highly regarded areas of data management research. Their framework broadly applies to data analytic tasks with areas of application that include databases, network monitoring, usage analytics in internet products, natural language processing and machine learning.