Publikation: Visual Analytics Techniques for Large Multi-Attribute Time Series Data
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2008
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BÖRNER, Katy, ed. and others. Visualization and Data Analysis 2008. SPIE, 2008, pp. 680908-680908-10. SPIE Proceedings. 6809. Available under: doi: 10.1117/12.768568
Zusammenfassung
Time series data commonly occur when variables are monitored over time. Many real-world applications involve the comparison of long time series across multiple variables (multi-attributes). Often business people want to compare this year s monthly sales with last year s sales to make decisions. Data warehouse administrators (DBAs) want to know their daily data loading job performance. DBAs need to detect the outliers early enough to act upon them. In this paper, two new visual analytic techniques are introduced: The cell-based Visual Time Series highlight significant changes over time within complex data sets and the new Visual Content Query facilitates finding the contents and histories of exceptions, which leads to root cause identification. We show examples of using these techniques to mine customer credit card fraud data to illustrate the wide applicability and usefulness of these techniques.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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visual analytics, multi-attribute data, time series, visual content query, contents and relationships
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Electronic Imaging 2008, San Jose, CA
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ISO 690
HAO, Ming C., Umeshwar DAYAL, Daniel A. KEIM, 2008. Visual Analytics Techniques for Large Multi-Attribute Time Series Data. Electronic Imaging 2008. San Jose, CA. In: BÖRNER, Katy, ed. and others. Visualization and Data Analysis 2008. SPIE, 2008, pp. 680908-680908-10. SPIE Proceedings. 6809. Available under: doi: 10.1117/12.768568BibTex
@inproceedings{Hao2008-01-27Visua-5447,
year={2008},
doi={10.1117/12.768568},
title={Visual Analytics Techniques for Large Multi-Attribute Time Series Data},
number={6809},
publisher={SPIE},
series={SPIE Proceedings},
booktitle={Visualization and Data Analysis 2008},
pages={680908--680908-10},
editor={Börner, Katy},
author={Hao, Ming C. and Dayal, Umeshwar and Keim, Daniel A.}
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