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

Dateien

EI108_EI107_32Submit.pdfGröße: 1.93 MBDownloads: 360

Datum

2008

Autor:innen

Hao, Ming C.

Dayal, Umeshwar

Keim, Daniel A.

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

BÖRNER, Katy, ed. and others. Visualization and Data Analysis 2008. SPIE, 2008, pp. 680908-680908-10. SPIE Proceedings. 6809. Available under: doi: 10.1117/12.768568

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.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

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

Konferenz

Electronic Imaging 2008, San Jose, CA

Zitieren

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: 10.1117/12.768568

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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Universitätsbibliographie

Ja