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VAET: A Visual Analytics Approach for E-Transactions Time-Series.

Cong Xie, Wei Chen, Xinxin Huang

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    |September 11, 2015
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    Summary
    This summary is machine-generated.

    This study introduces Visual Analysis of E-transaction Time-Series (VAET), a system for exploring large e-transaction datasets. VAET identifies and visualizes time-varying transaction saliency, aiding analysts in discovering significant patterns.

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    Area of Science:

    • Computer Science
    • Information Visualization
    • Data Mining

    Background:

    • E-transaction time-series analysis often overlooks significant temporal patterns within large datasets.
    • Identifying time-stamped, situation-relevant transactions requires advanced analytical tools.

    Purpose of the Study:

    • To propose Visual Analysis of E-transaction Time-Series (VAET), an interactive visual analytics system.
    • To enable analysts to effectively explore large e-transaction datasets and uncover time-varying transaction insights.

    Main Methods:

    • Developed VAET, a visual analytics system for interactive exploration of e-transaction time-series.
    • Employed a probabilistic decision tree learner to estimate transaction saliency based on analyst-provided training samples.
    • Introduced KnotLines for compact visual representation of temporal variations and contextual connections of transactions.

    Main Results:

    • VAET effectively estimates transaction saliency in large time-series datasets.
    • The Time-of-Saliency (TOS) map allows exploration of transactions at various time granularities.
    • KnotLines provide an intuitive method for investigating interesting transactions.

    Conclusions:

    • VAET demonstrates effectiveness in identifying significant time-varying patterns in large e-transaction datasets.
    • The system enhances analysts' ability to discover and investigate important transactions through interactive visualization.
    • User and case studies confirm VAET's utility with real-world, large-scale e-transaction data.