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No pane, no gain: efficient evaluation of sliding-window aggregates over data streams

Authors:

Jin Li,

David Maier,

Kristin Tufte,

Vassilis Papadimos,

Peter A. TuckerAuthors Info & Claims

ACM SIGMOD Record, Volume 34, Issue 1

Pages 39 - 44

https://doi.org/10.1145/1058150.1058158

Published: 01 March 2005 Publication History

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Abstract

Windows queries are proving essential to data-stream processing. In this paper, we present an approach for evaluating sliding-window aggregate queries that reduces both space and computation time for query execution. Our approach divides overlapping windows into disjoint panes, computes sub-aggregates over each pane, and "rolls up" the pane-aggregates to computer window-aggregates. Our experimental study shows that using panes has significant performance benefits.

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Lam KZhao XZhu CKuo T(2024)MVLevelDB+: Meeting Relative Consistency Requirements of Temporal Queries in Sensor Stream DatabasesACM Transactions on Embedded Computing Systems10.1145/369478724:1(1-26)Online publication date: 4-Sep-2024
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Östgren MSourdis I(2024)A Parallel Hash Table for Streaming ApplicationsProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676951(297-308)Online publication date: 14-Oct-2024
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Meldrum MCarbone P(2024)μWheel: Aggregate Management for Streams and QueriesProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666031(54-65)Online publication date: 24-Jun-2024
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Index Terms

No pane, no gain: efficient evaluation of sliding-window aggregates over data streams
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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Published In

ACM SIGMOD Record Volume 34, Issue 1

March 2005

86 pages

ISSN:0163-5808

DOI:10.1145/1058150

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Association for Computing Machinery

New York, NY, United States

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Published: 01 March 2005

Published in SIGMOD Volume 34, Issue 1

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Lam KZhao XZhu CKuo T(2024)MVLevelDB+: Meeting Relative Consistency Requirements of Temporal Queries in Sensor Stream DatabasesACM Transactions on Embedded Computing Systems10.1145/369478724:1(1-26)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3694787
Östgren MSourdis I(2024)A Parallel Hash Table for Streaming ApplicationsProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676951(297-308)Online publication date: 14-Oct-2024
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Martinez JRaschia GSerra ESpezzano F(2024)Revisiting Optimal Window Aggregation in Data Streams: The Prefix-Sum ApproachProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679573(1660-1669)Online publication date: 21-Oct-2024
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Bou SAmagasa TKitagawa H(2024)O(1)-Time Complexity for Fixed Sliding-Window Aggregation Over Out-of-Order Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341956636:11(6745-6757)Online publication date: Nov-2024
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Payload pendulation and position control systems for an offshore container crane with adaptive‐gain sliding mode control

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Payload pendulation and position control systems for an offshore container crane with adaptive‐gain sliding mode control

Reducing the mast vibration of single-mast stacker cranes by gain-scheduled control

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