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Data Summarization and Distributed Computation

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Graham CormodeAuthors Info & Claims

PODC '18: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing

Pages 167 - 168

https://doi.org/10.1145/3212734.3212795

Published: 23 July 2018 Publication History

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Abstract

The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function. In this talk, I'll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving robust regression problems over large, distributed data sets.

References

[1]

Graham Cormode, Charlie Dickens, and David Woodruff. Leveraging well-conditioned bases: Streaming and distributed summaries in minkowski p-norms. In International Conference on Machine Learning, 2018.

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[2]

Zengfeng Huang, Ke Yi, and Qin Zhang. Randomized algorithms for tracking distributed count, frequencies, and ranks. In ACM Principles of Database Systems, pages 295--306, 2012.

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[3]

Yu Zhang, Srikanta Tirthapura, and Graham Cormode. Learning graphical models from a distributed stream. In IEEE International Conference on Data Engineering, 2018.

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Cited By

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Chen QHong YWu YYang TCui B(2024)CodingSketch: A Hierarchical Sketch with Efficient Encoding and Recursive Decoding2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00130(1592-1605)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00130
Liu ZZhang YZhu YZhang RYang TXie KWang SLi TCui B(2023)TreeSensing: Linearly Compressing Sketches with FlexibilityProceedings of the ACM on Management of Data10.1145/35889101:1(1-28)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588910

Index Terms

Data Summarization and Distributed Computation
1. Theory of computation
  1. Design and analysis of algorithms
    1. Streaming, sublinear and near linear time algorithms
      1. Sketching and sampling

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Published In

PODC '18: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing

July 2018

512 pages

ISBN:9781450357951

DOI:10.1145/3212734

General Chairs:
Calvin Newport
Georgetown University, USA
,
Idit Keidar
Technion, Israel

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 23 July 2018

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European Research Council

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PODC '18

PODC '18: ACM Symposium on Principles of Distributed Computing

July 23 - 27, 2018

Egham, United Kingdom

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PODC '18 Paper Acceptance Rate 41 of 163 submissions, 25%;

Overall Acceptance Rate 740 of 2,477 submissions, 30%

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Chen QHong YWu YYang TCui B(2024)CodingSketch: A Hierarchical Sketch with Efficient Encoding and Recursive Decoding2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00130(1592-1605)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00130
Liu ZZhang YZhu YZhang RYang TXie KWang SLi TCui B(2023)TreeSensing: Linearly Compressing Sketches with FlexibilityProceedings of the ACM on Management of Data10.1145/35889101:1(1-28)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588910

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Data Summarization with Social Contexts

Data decoys for confidentiality in a distributed computation: matrix multiplication

Distributed Data Clustering in the Context of the Internet of Things: A Data Traffic Reduction Approach

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