Professor Zhewei Wei Won the 2022 ACM PODS Alberto O. Mendelzon Test-of-Time Award

Date:2022-07-22 Visits:

Recently, the top database conference ACM SIGMOD/PODS 2022 was held in Philadelphia, USA. Professor Zhewei Wei from Gaoling School of Artificial Intelligence, Renmin University of China won the ACM PODS Alberto O. Mendelzon Test-of-Time Award for his paper “Mergeable Summaries” published in PODS 2012.

The ACM PODS Alberto O. Mendelzon Test-of-Time Award was established in 2007 and was awarded for the first time in 2008. It is awarded every year to a paper or a small number of papers published in the PODS proceedings ten years prior that had the most impact in terms of research, methodology, or transfer to practice over the intervening decade.

魏哲巍 最佳论文奖.png

Official Comments by the Award Committee:

“This paper initiated the study of mergeability of sketching methods. This included analysing the mergeability of the popular existing summaries, and developing new mergeable sketching algorithms for quantiles, an aggregate that is important in industry, and also for heavy hitters and geometric summaries. The impact within streaming implementations has been extremely significant: mergeability is now an essential property of sketches, and stands as a core principle of the Apache Data Sketches project as well as other products in industry. The paper has also proved increasingly influential within academic research, both within databases and algorithms. Follow-ups include the best paper award winning paper at PODS ’21. Overall, this paper has represented an important new direction both for theory and industry.”

In this paper, Prof. Zhewei Wei first proves the mergeability of the Misra-Gries (MG) summaries, and proposes a merging algorithm of the MG summaries that preserves the summary size and approximation error. This merging algorithm has been included in many data streaming textbooks and taught in various graduate courses of top universities such as MIT and UC Berkeley.

Authors:Pankaj K. Agarwal, Graham Cormode, Zengfeng Huang, Jeff M. Phillips, Zhewei Wei and Ke Yi.

(The authors are ordered alphabetically, following the convention of theoretical computer science.)

The paper is available at:

A short bio of Prof. Zhewei Wei:


Zhewei Wei is a professor at Gaoling School of Artificial Intelligence, Renmin University of China. He received his BSc degree at School of Mathematical Sciences, Peking University in 2008, and his PhD degree at Department of Computer Science and Engineering, the Hong Kong University of Science and Technology in 2012. After that, he worked as a Postdoc at MADALGO, Aarhus University, from 2012 to 2014. He has joined Renmin University of China and worked as Associate Professor since 2014, and are exceptionally promoted as professor in 2019. Zhewei’s research interests lie in the area of algorithms for massive data and database management. He has published over 50 papers in top-tier journals and conferences, including SIGMOD, VLDB, KDD, ICML, NeurIPS, SODA and TALG. Zhewei served as the Proceeding Chair of SIGMOD/PODS 2020 and ICDT 2021. He is also the PC member of various top conferences, such as VLDB, KDD, ICDE, ICML and NeurIPS.