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Article

Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets

Authors:

Vandana Pursnani Janeja,

Vijayalakshmi AtluriAuthors Info & Claims

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

Pages 576 - 583

https://doi.org/10.1145/967900.968020

Published: 14 March 2004 Publication History

Abstract

The behavior of spatial objects is under the influence of nearby spatial processes. Therefore in order to perform any type of spatial analysis we need to take into account not only the spatial relationships among objects but also the underlying spatial processes and other spatial features in the vicinity that influence the behavior of a given spatial object. In this paper, we address the outlier detection by refining the concept of a neighborhood of an object, which essentially characterizes similarly behaving objects into one neighborhood. This similarity is quantified in terms of the spatial relationships among the objects and other semantic relationships based on the spatial processes and spatial features in their vicinity. These spatial features could be natural such as a stream, and vegetation, or man-made such as a bridge, railroad, and chemical factory. The paper also addresses the identification of spatio-temporal outliers in high dimensions, in their neighborhood.

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Cited By

Allka XFerrer-Cid PBarcelo-Ordinas JGarcia-Vidal J(2024)Leveraging Spatiotemporal Correlations With Recurrent Autoencoders for Sensor Anomaly DetectionIEEE Internet of Things Journal10.1109/JIOT.2024.341652511:19(31144-31152)Online publication date: 1-Oct-2024
https://doi.org/10.1109/JIOT.2024.3416525
Pereira FSofia R(2022)An Analysis of ML-Based Outlier Detection from Mobile Phone TrajectoriesFuture Internet10.3390/fi1501000415:1(4)Online publication date: 23-Dec-2022
https://doi.org/10.3390/fi15010004
de C. Costa RMoreira J(2022)Automatic Quality Improvement of Data on the Evolution of 2D RegionsAdvanced Data Mining and Applications10.1007/978-3-030-95408-6_22(288-300)Online publication date: 31-Jan-2022
https://doi.org/10.1007/978-3-030-95408-6_22
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Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

March 2004

1733 pages

ISBN:1581138121

DOI:10.1145/967900

Conference Chair:
Hisham M. Haddad
Kennesaw State University
,
Program Chairs:
Andrea Omicini
Università degli Studi di Bologna a Cesena
,
Roger L. Wainwright
University of Tulsa
,
Publications Chair:
Lorie M. Liebrock
New Mexico Institute of Mining and Technology

Copyright © 2004 ACM.

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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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2004

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SAC04: The 2004 ACM Symposium on Applied Computing

March 14 - 17, 2004

Nicosia, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Allka XFerrer-Cid PBarcelo-Ordinas JGarcia-Vidal J(2024)Leveraging Spatiotemporal Correlations With Recurrent Autoencoders for Sensor Anomaly DetectionIEEE Internet of Things Journal10.1109/JIOT.2024.341652511:19(31144-31152)Online publication date: 1-Oct-2024
https://doi.org/10.1109/JIOT.2024.3416525
Pereira FSofia R(2022)An Analysis of ML-Based Outlier Detection from Mobile Phone TrajectoriesFuture Internet10.3390/fi1501000415:1(4)Online publication date: 23-Dec-2022
https://doi.org/10.3390/fi15010004
de C. Costa RMoreira J(2022)Automatic Quality Improvement of Data on the Evolution of 2D RegionsAdvanced Data Mining and Applications10.1007/978-3-030-95408-6_22(288-300)Online publication date: 31-Jan-2022
https://doi.org/10.1007/978-3-030-95408-6_22
Hendawi AShen JSabbineni SSong YCao PZhang ZKrumm JAli M(2020)Noise Patterns in GPS Trajectories2020 21st IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM48529.2020.00040(178-185)Online publication date: Jun-2020
https://doi.org/10.1109/MDM48529.2020.00040
Karadayi Y(2020)Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep LearningAdvanced Analytics and Learning on Temporal Data10.1007/978-3-030-39098-3_13(167-182)Online publication date: 23-Jan-2020
https://doi.org/10.1007/978-3-030-39098-3_13
Anastasiu DKarypis G(2017)Efficient identification of Tanimoto nearest neighborsInternational Journal of Data Science and Analytics10.1007/s41060-017-0064-z4:3(153-172)Online publication date: 2-Aug-2017
https://doi.org/10.1007/s41060-017-0064-z
Janeja VAdam N(2017)Homeland Security and Spatial Data MiningEncyclopedia of GIS10.1007/978-3-319-17885-1_568(859-866)Online publication date: 12-May-2017
https://doi.org/10.1007/978-3-319-17885-1_568
Shi YDeng MYang XLiu Q(2016)A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local DensityTransactions in GIS10.1111/tgis.1220821:2(376-405)Online publication date: 27-Jun-2016
https://doi.org/10.1111/tgis.12208
Anastasiu DKarypis G(2016)Efficient Identification of Tanimoto Nearest Neighbors2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2016.23(156-165)Online publication date: Oct-2016
https://doi.org/10.1109/DSAA.2016.23
Janeja VAdam N(2016)Homeland Security and Spatial Data MiningEncyclopedia of GIS10.1007/978-3-319-23519-6_568-2(1-8)Online publication date: 26-May-2016
https://doi.org/10.1007/978-3-319-23519-6_568-2
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