Current Trends in Data Summaries-Reference-Cited by-同舟云学术

Current Trends in Data Summaries

Published:2022-01-31 Issue:4 Volume:50 Page:6-15
ISSN:0163-5808
Container-title:ACM SIGMOD Record
language:en
Short-container-title:SIGMOD Rec.

Author:

Cormode Graham

Abstract

The research area of data summarization seeks to find small data structures that can be updated flexibly, and answer certain queries on the input accurately. Summaries are widely used across the area of data management, and are studied from both theoretical and practical perspectives. They are the subject of ongoing research to improve their performance and broaden their applicability. In this column, recent developments in data summarization are surveyed, with the intent of inspiring further advances.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems,Software

Link

https://dl.acm.org/doi/pdf/10.1145/3516431.3516433

Reference62 articles.

1. Adversarial laws of large numbers and optimal regret in online classification

2. Apple Differential Privacy Team . Learning with privacy at scale. https://docs-assets.developer.apple. com/ml-research/papers/ learning-with-privacy-at-scale.pdf , 2017 . Apple Differential Privacy Team. Learning with privacy at scale. https://docs-assets.developer.apple. com/ml-research/papers/ learning-with-privacy-at-scale.pdf, 2017.

3. R. B. Basat , M. Mitzenmacher , and S. Vargaftik . How to send a real number using a single bit (and some shared randomness). In 48th International Colloquium on Automata, Languages, and Programming, ICALP 2021 , volume 198 of LIPIcs, pages 25: 1 -- 25 :20. Schloss Dagstuhl - Leibniz-Zentrum f¨ur Informatik , 2021 . R. B. Basat, M. Mitzenmacher, and S. Vargaftik. How to send a real number using a single bit (and some shared randomness). In 48th International Colloquium on Automata, Languages, and Programming, ICALP 2021, volume 198 of LIPIcs, pages 25:1--25:20. Schloss Dagstuhl - Leibniz-Zentrum f¨ur Informatik, 2021.

4. Practical locally private heavy 12 SIGMOD Record, December 2021 (Vol. 50, No. 4) hitters;Bassily R.;J. Mach. Learn. Res.,2020

5. A Framework for Adversarially Robust Streaming Algorithms

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