Visual analytics techniques for large multi-attribute time series data

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

doi:10.1117/12.768568

28 January 2008 Visual analytics techniques for large multi-attribute time series data

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

Proceedings Volume 6809, Visualization and Data Analysis 2008; 680908 (2008) https://doi.org/10.1117/12.768568
Event: Electronic Imaging, 2008, San Jose, California, United States

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 color cell-based Visual Time Series Line Charts and Maps highlight significant changes over time in a long time series data and the new Visual Content Query facilitates finding the contents and histories of interesting patterns and anomalies, which leads to root cause identification. We have applied both methods to two real-world applications to mine enterprise data warehouse and customer credit card fraud data to illustrate the wide applicability and usefulness of these techniques.

Citation Download Citation

Ming C. Hao, Umeshwar Dayal, and Daniel A. Keim "Visual analytics techniques for large multi-attribute time series data", Proc. SPIE 6809, Visualization and Data Analysis 2008, 680908 (28 January 2008); https://doi.org/10.1117/12.768568

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