research-article

Anomaly detection and spatio-temporal analysis of global climate system

Authors:

Mahashweta Das,

Srinivasan ParthasarathyAuthors Info & Claims

SensorKDD '09: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data

Pages 142 - 150

https://doi.org/10.1145/1601966.1601989

Published: 28 June 2009 Publication History

Abstract

Knowledge discovery from temporal, spatial and spatio-temporal data is pivotal for understanding and predicting the behavior of Earth's ecosystem model. An important influence leaving its impact on the ecosystem is the global climate system. In this paper, the Earth Science data that we have analyzed consists of daily global air temperature and precipitation measurements, aggregated from heterogeneous sensors for fifty years (1950--1999). The enormous amount of data that is available for analysis requires employment of data mining techniques for discovering interesting patterns, detecting significant changes and extracting meaningful insights from the data. Our work considers the problem of detecting anomalous (abnormal or unexpected) behavior in the global climate system, discovering teleconnection patterns and providing consequential insights to the analysts.

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https://doi.org/10.1109/ICDE60146.2024.00116
Liu TZhou ZYang L(2024)Layered isolation forest: A multi-level subspace algorithm for improving isolation forestNeurocomputing10.1016/j.neucom.2024.127525581(127525)Online publication date: May-2024
https://doi.org/10.1016/j.neucom.2024.127525
Wahyono THeryadi Y(2022)Machine Learning Applications for Anomaly DetectionResearch Anthology on Machine Learning Techniques, Methods, and Applications10.4018/978-1-6684-6291-1.ch008(107-136)Online publication date: 13-May-2022
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Index Terms

Anomaly detection and spatio-temporal analysis of global climate system
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

SensorKDD '09: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data

June 2009

150 pages

ISBN:9781605586687

DOI:10.1145/1601966

Conference Chairs:
Olufemi A. Omitaomu
Oak Ridge National Laboratory, TN
,
Auroop R. Ganguly
Oak Ridge National Laboratory, TN
,
Joao Gama
University of Porto, Portugal
,
Ranga Raju Vatsavai
Oak Ridge National Laboratory, TN
,
Nitesh V. Chawla
University of Notre Dame, IN
,
Mohamed Medhat Gaber
Monash University, Australia

Copyright © 2009 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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data
Geographic Information Science and Technology (GIST) Group at Oak Ridge National Laboratory
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
Computational Sciences and Engineering (CSE) Division at the Oak Ridge National Laboratory
Cooperating Objects Network of Excellence (CONET)

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Association for Computing Machinery

New York, NY, United States

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Published: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28, 2009

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Cited By

Sánchez Vinces BCordeiro RFaloutsos C(2024)Mccatch: Scalable Microcluster Detection in Dimensional and Nondimensional Datasets2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00116(1407-1420)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00116
Liu TZhou ZYang L(2024)Layered isolation forest: A multi-level subspace algorithm for improving isolation forestNeurocomputing10.1016/j.neucom.2024.127525581(127525)Online publication date: May-2024
https://doi.org/10.1016/j.neucom.2024.127525
Wahyono THeryadi Y(2022)Machine Learning Applications for Anomaly DetectionResearch Anthology on Machine Learning Techniques, Methods, and Applications10.4018/978-1-6684-6291-1.ch008(107-136)Online publication date: 13-May-2022
https://doi.org/10.4018/978-1-6684-6291-1.ch008
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https://doi.org/10.1007/s10584-022-03444-6
Allen-Dumas MXu HKurte KRastogi D(2021)Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water HazardsFrontiers in Water10.3389/frwa.2020.5623042Online publication date: 29-Jan-2021
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Djenouri YDjenouri DLin J(2021)Trajectory Outlier DetectionACM Transactions on Knowledge Discovery from Data10.1145/342586715:2(1-28)Online publication date: 10-Feb-2021
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Foorthuis R(2021)On the nature and types of anomalies: a review of deviations in dataInternational Journal of Data Science and Analytics10.1007/s41060-021-00265-112:4(297-331)Online publication date: 4-Aug-2021
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