NDLI: Visually mining and monitoring massive time series

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Visually mining and monitoring massive time series

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Visually mining and monitoring massive time series

Content Provider	ACM Digital Library
Author	Lankford, Jeffrey P. Nystrom, Donna M. Lonardi, Stefano Keogh, Eamonn Lin, Jessica
Abstract	Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/no-go decision. The cost of a false positive, allowing a launch in spite of a fault, or a false negative, stopping a potentially successful launch, can be measured in the tens of millions of dollars, not including the cost in morale and other more intangible detriments. The Aerospace Corporation is responsible for providing engineering assessments critical to the go/no-go decision for every Department of Defense space vehicle. These assessments are made by constantly monitoring streaming telemetry data in the hours before launch. We will introduce VizTree, a novel time-series visualization tool to aid the Aerospace analysts who must make these engineering assessments. VizTree was developed at the University of California, Riverside and is unique in that the same tool is used for mining archival data and monitoring incoming live telemetry. The use of a single tool for both aspects of the task allows a natural and intuitive transfer of mined knowledge to the monitoring task. Our visualization approach works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic datasets.
Starting Page	460
Ending Page	469
Page Count	10
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014104
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Visualization Motif discovery Time series Pattern discovery Anomaly detection
Content Type	Text
Resource Type	Article

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