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Querying and mining data streams: you only get one look a tutorial

Authors:

Minos Garofalakis,

Johannes Gehrke,

Rajeev RastogiAuthors Info & Claims

SIGMOD '02: Proceedings of the 2002 ACM SIGMOD international conference on Management of data

Page 635

https://doi.org/10.1145/564691.564794

Published: 03 June 2002 Publication History

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Fan ZWang RCai YZhang RYang TWu YCui BUhlig S(2023)OneSketch: A Generic and Accurate Sketch for Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327802835:12(12887-12901)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2023.3278028
Seyfi MNayak RXu Y(2023)Mining Discriminative Itemsets Over Data Streams Using Efficient Sliding WindowSN Computer Science10.1007/s42979-023-01887-x4:5Online publication date: 27-Jun-2023
https://doi.org/10.1007/s42979-023-01887-x
Chen CWang B(2022)Random-Positioned License Plate Recognition Using Hybrid Broad Learning System and Convolutional NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301193723:1(444-456)Online publication date: Jan-2022
https://doi.org/10.1109/TITS.2020.3011937
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Querying and mining data streams: you only get one look a tutorial
1. Information systems
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

SIGMOD '02: Proceedings of the 2002 ACM SIGMOD international conference on Management of data

June 2002

654 pages

ISBN:1581134975

DOI:10.1145/564691

Conference Chair:
Bongki Moon
University of Wisconsin - Madison
,
General Chair:
David DeWitt
University of Wisconsin - Madison
,
Program Chair:
Michael Franklin
University of California, Berkeley

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 03 June 2002

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SIGMOD/PODS02

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SIGMOD/PODS02: International Conference on Management of Data and Symposium on Principles Database and Systems

June 3 - 6, 2002

Wisconsin, Madison

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SIGMOD '02 Paper Acceptance Rate 42 of 240 submissions, 18%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Fan ZWang RCai YZhang RYang TWu YCui BUhlig S(2023)OneSketch: A Generic and Accurate Sketch for Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327802835:12(12887-12901)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2023.3278028
Seyfi MNayak RXu Y(2023)Mining Discriminative Itemsets Over Data Streams Using Efficient Sliding WindowSN Computer Science10.1007/s42979-023-01887-x4:5Online publication date: 27-Jun-2023
https://doi.org/10.1007/s42979-023-01887-x
Chen CWang B(2022)Random-Positioned License Plate Recognition Using Hybrid Broad Learning System and Convolutional NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301193723:1(444-456)Online publication date: Jan-2022
https://doi.org/10.1109/TITS.2020.3011937
Garcia-Alvarado COrdonez C(2022)In-DBMS K-means Clustering for Binary Streams2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020322(1987-1996)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020322
Tareq MSundararajan EHarwood ABakar A(2022)A Systematic Review of Density Grid-Based Clustering for Data StreamsIEEE Access10.1109/ACCESS.2021.313470410(579-596)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3134704
Kang DGuibas JBailis PHashimoto TSun YZaharia M(2021)Accelerating approximate aggregation queries with expensive predicatesProceedings of the VLDB Endowment10.14778/3476249.347628514:11(2341-2354)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476285
Matri PRoss R(2021)Neon: Low-Latency Streaming Pipelines for HPC2021 IEEE 14th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD53861.2021.00089(698-707)Online publication date: Sep-2021
https://doi.org/10.1109/CLOUD53861.2021.00089
Bahri MBifet AGama JGomes HManiu S(2021)Data stream analysis: Foundations, major tasks and toolsWIREs Data Mining and Knowledge Discovery10.1002/widm.140511:3Online publication date: 2-Mar-2021
https://doi.org/10.1002/widm.1405
Arun SSudharson K(2020)DEFECT: discover and eradicate fool around node in emergency network using combinatorial techniquesJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02606-714:5(5995-6006)Online publication date: 31-Oct-2020
https://doi.org/10.1007/s12652-020-02606-7
Roriz Junior MOlivieri BEndler M(2019)DG2CEP: a near real-time on-line algorithm for detecting spatial clusters large data streams through complex event processingJournal of Internet Services and Applications10.1186/s13174-019-0107-x10:1Online publication date: 15-Apr-2019
https://doi.org/10.1186/s13174-019-0107-x
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Querying and Mining Uncertain Data Streams

Sketch-based querying of distributed sliding-window data streams

Mining of Frequent Itemsets from Streams of Uncertain Data

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Querying and Mining Uncertain Data Streams

Sketch-based querying of distributed sliding-window data streams

Mining of Frequent Itemsets from Streams of Uncertain Data

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