Visual Analytics Techniques for Large Multi-Attribute Time Series Data

Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.

Visual Analytics Techniques for Large Multi-Attribute Time Series Data

Files

EI108_EI107_32Submit.pdf(1.93 MB)

Date

2008

Authors

Hao, Ming C.

Dayal, Umeshwar

Keim, Daniel A.

Publication type

Contribution to a conference collection

Published in

Visualization and Data Analysis 2008 / Börner, Katy et al. (ed.). - SPIE, 2008. - (SPIE Proceedings ; 6809). - pp. 680908-680908-10

Abstract

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.

Subject (DDC)

004 Computer Science

Keywords

visual analytics,multi-attribute data,time series,visual content query,contents and relationships

Conference

Electronic Imaging 2008, San Jose, CA

Cite This

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, pp. 680908-680908-10. Available under: doi: 10.1117/12.768568

BibTex

@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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Bibliography of Konstanz

Yes