Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

Area of Science:

• Data Science
• Information Visualization
• Human-Computer Interaction

Background:

• The increasing volume and variety of big data present significant challenges for visual analytics.
• Temporal event sequence analytics is particularly affected by the sheer number of events and diverse patterns.
• Effective visual analytic tools are crucial for extracting valuable insights from complex datasets across various domains.

Purpose of the Study:

• To address the challenges posed by big data in visual analytics, specifically for temporal event sequence data.
• To introduce and categorize 15 strategies designed to sharpen analytic focus.
• To reduce data volume and pattern variety in visual analytic tasks.

Main Methods:

• Development of 15 distinct strategies categorized into four groups: extraction, temporal folding, pattern simplification, and iterative strategies.
• Illustration of each strategy's application and impact on data volume and pattern variety.
• Gathering examples from 20 case studies, including original research, literature review, and expert interviews.

Main Results:

• The proposed strategies effectively reduce data volume and pattern variety in temporal event sequence analytics.
• Four distinct groups of strategies (extraction, temporal folding, pattern simplification, iterative) offer a structured approach to data management.
• Case study examples demonstrate the practical utility and impact of these strategies.

Conclusions:

• The presented strategies provide analysts with actionable methods to manage complex temporal event sequence data.
• Combining these strategies can further enhance analytic focus and efficiency.
• Feedback from senior analysts indicates the practical value and applicability of these techniques in real-world scenarios.