research-article

Interactive outlier exploration in big data streams

Authors:

Elke A. RundensteinerAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 7, Issue 13

Pages 1621 - 1624

https://doi.org/10.14778/2733004.2733045

Published: 01 August 2014 Publication History

Abstract

We demonstrate our VSOutlier system for supporting interactive exploration of outliers in big data streams. VSOutlier not only supports a rich variety of outlier types supported by innovative and efficient outlier detection strategies, but also provides a rich set of interactive interfaces to explore outliers in real time. Using the stock transactions dataset from the US stock market and the moving objects dataset from MITRE, we demonstrate that the VSOutlier system enables analysts to more efficiently identify, understand, and respond to phenomena of interest in near real-time even when applied to high volume streams.

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Cited By

Heddes MNunes IGivargis TNicolau A(2024)Convolution and Cross-Correlation of Count Sketches Enables Fast Cardinality Estimation of Multi-Join QueriesProceedings of the ACM on Management of Data10.1145/36549322:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654932
Zhao YChen GJia Z(2022)TODProceedings of the VLDB Endowment10.14778/3570690.357070316:3(546-560)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.14778/3570690.3570703
Yoon SShin YLee JLee BLi GLi ZIdreos SSrivastava D(2021)Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and QueriesProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452810(2063-2075)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452810
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Interactive outlier exploration in big data streams
1. Information systems
  1. Information systems applications

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 7, Issue 13

August 2014

466 pages

ISSN:2150-8097

Editors:
H. V. Jagadish
University of Michigan
,
Aoying Zhou
East Normal University, China

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VLDB Endowment

Publication History

Published: 01 August 2014

Published in PVLDB Volume 7, Issue 13

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Cited By

Heddes MNunes IGivargis TNicolau A(2024)Convolution and Cross-Correlation of Count Sketches Enables Fast Cardinality Estimation of Multi-Join QueriesProceedings of the ACM on Management of Data10.1145/36549322:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654932
Zhao YChen GJia Z(2022)TODProceedings of the VLDB Endowment10.14778/3570690.357070316:3(546-560)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.14778/3570690.3570703
Yoon SShin YLee JLee BLi GLi ZIdreos SSrivastava D(2021)Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and QueriesProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452810(2063-2075)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452810
Yoon SLee JLee B(2019)NETSProceedings of the VLDB Endowment10.14778/3342263.334226912:11(1303-1315)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.14778/3342263.3342269
Abuzaid FBailis PDing JGan EMadden SNarayanan DRong KSuri S(2018)MacroBaseACM Transactions on Database Systems10.1145/327646343:4(1-45)Online publication date: 6-Dec-2018
https://dl.acm.org/doi/10.1145/3276463
Gong SZhang YYu G(2018)Clustering stream data by exploring the evolution of density mountainProceedings of the VLDB Endowment10.1145/3164135.316413611:4(393-405)Online publication date: 5-Oct-2018
Gong SZhang YYu G(2017)Clustering stream data by exploring the evolution of density mountainProceedings of the VLDB Endowment10.1145/3186728.316413611:4(393-405)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1145/3186728.3164136
Bailis PGan EMadden SNarayanan DRong KSuri SChirkova RYang JSuciu D(2017)MacroBaseProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3035928(541-556)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3035928
Xu CKällström ERisch TLindström JHåkansson LLarsson J(2017)Scalable validation of industrial equipment using a functional DSMSJournal of Intelligent Information Systems10.1007/s10844-016-0427-248:3(553-577)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10844-016-0427-2
Tran LFan LShahabi C(2016)Distance-based outlier detection in data streamsProceedings of the VLDB Endowment10.14778/2994509.29945269:12(1089-1100)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.14778/2994509.2994526
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