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Forensic Analysis of Time Series

K. (Ponnu) Ponnambalam (2010)
University of Waterloo
DOI: 10.14796/JWMM.R236-27
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Abstract

With the advent of automatic sensors for detection and data collection (for example, SCADA systems), it is now possible to acquire a large number of time series of critical data. In urban water and sewer systems, monitoring stations can collect data on water quantity and quality (for example, dissolved oxygen, electric conductivity, pH and turbidity, among others). The motivation for such data collection usually is to analyze if the systems are working properly. New analytical techniques are needed in order to efficiently analyze such large quantities of data and to answer questions of forensic nature (for example, how well the systems are working and whether any of the components are faulty). An

automatic inference system consisting of feature extraction, clustering, and classification steps is developed to answer categorical questions using the large amount of data.

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PAPER INFO

Identification

CHI ref #: R236-27 781
Volume: 18
DOI: 10.14796/JWMM.R236-27
Cite as: CHI JWMM 2010;R236-27

Publication History

Received: N/A
Accepted: N/A
Published: February 15, 2010

Status

# reviewers: 2
Version: Final published

Copyright

© 2010 CHI. Some rights reserved.

The Journal of Water Management Modeling is an open-access (OA) publication. Open access means that articles and papers are available without barriers to all who could benefit from them. Practically speaking, all published works will be available to a worldwide audience, free, immediately on publication.

JWMM content can be downloaded, printed, copied, distributed, and linked-to, provided full attribution to both CHI and the author is given.

JWMM content may not be re-published, either online or in print, without prior written consent of CHI. As such, JWMM can be considered a Gold, Gratis OA journal.


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K. (Ponnu) Ponnambalam

University of Waterloo, Waterloo, ON, Canada

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