article

Data streams: algorithms and applications

Author:

S. MuthukrishnanAuthors Info & Claims

Foundations and Trends® in Theoretical Computer Science, Volume 1, Issue 2

Pages 117 - 236

https://doi.org/10.1561/0400000002

Published: 01 August 2005 Publication History

Abstract

In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of [1].

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Cited By

Czumaj AMishra GMukherjee AKuznetsov PGelles ROlivetti D(2024)Streaming Graph Algorithms in the Massively Parallel Computation ModelProceedings of the 43rd ACM Symposium on Principles of Distributed Computing10.1145/3662158.3662770(496-507)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3662158.3662770
Tench DWest EZhang VBender MChowdhury ADelayo DDellas JFarach-Colton MSeip TZhang K(2024)GraphZeppelin: How to Find Connected Components (Even When Graphs Are Dense, Dynamic, and Massive)ACM Transactions on Database Systems10.1145/364384649:3(1-31)Online publication date: 16-May-2024
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Wang YWang YChen CBaeza-Yates RBonchi F(2024)DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding WindowsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671694(3255-3266)Online publication date: 25-Aug-2024
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Index Terms

Data streams: algorithms and applications
1. Mathematics of computing
  1. Information theory
    1. Coding theory
2. Security and privacy
  1. Cryptography
    1. Mathematical foundations of cryptography

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cover image Foundations and Trends® in Theoretical Computer Science

Foundations and Trends® in Theoretical Computer Science Volume 1, Issue 2

August 2005

120 pages

ISSN:1551-305X

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Published: 01 August 2005

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Czumaj AMishra GMukherjee AKuznetsov PGelles ROlivetti D(2024)Streaming Graph Algorithms in the Massively Parallel Computation ModelProceedings of the 43rd ACM Symposium on Principles of Distributed Computing10.1145/3662158.3662770(496-507)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3662158.3662770
Tench DWest EZhang VBender MChowdhury ADelayo DDellas JFarach-Colton MSeip TZhang K(2024)GraphZeppelin: How to Find Connected Components (Even When Graphs Are Dense, Dynamic, and Massive)ACM Transactions on Database Systems10.1145/364384649:3(1-31)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3643846
Wang YWang YChen CBaeza-Yates RBonchi F(2024)DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding WindowsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671694(3255-3266)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671694
Aslanyan L(2024)Sequential Data Classification under Dynamic EmissionPattern Recognition and Image Analysis10.1134/S105466182401004834:1(187-198)Online publication date: 1-Mar-2024
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Mirjalali KZarrabi-Zadeh H(2024)Massively parallel and streaming algorithms for balanced clusteringTheoretical Computer Science10.1016/j.tcs.2023.114296983:COnline publication date: 1-Feb-2024
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Huang CSellier F(2024)Semi-streaming Algorithms for Submodular Function Maximization Under b-Matching, Matroid, and Matchoid ConstraintsAlgorithmica10.1007/s00453-024-01272-x86:11(3598-3628)Online publication date: 14-Sep-2024
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Chen JHuang QKanj IXia G(2024)Nearly Time-Optimal Kernelization Algorithms for the Line-Cover Problem with Big DataAlgorithmica10.1007/s00453-024-01231-686:8(2448-2478)Online publication date: 1-Aug-2024
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Chakrabarty SMakarychev KOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Single-pass pivot algorithm for correlation clustering. keep it simple!Proceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666402(6412-6421)Online publication date: 10-Dec-2023
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Jayaram RWoodruff D(2023)Towards Optimal Moment Estimation in Streaming and Distributed ModelsACM Transactions on Algorithms10.1145/359649419:3(1-35)Online publication date: 24-Jun-2023
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Ulmer AAngelini MFekete JKohlhammer JMay T(2023)A Survey on Progressive VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334664130:9(6447-6467)Online publication date: 25-Dec-2023
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