document: Visually mining and monitoring massive time series 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 .
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