EventBox: A Novel Visual Encoding for Interactive Analysis of Temporal and Multivariate Attributes in Event Sequences

2025-07-19

AI Summary

Existing event sequence analysis methods often decouple temporal attributes from multivariate features, hindering holistic pattern discovery. This paper introduces EventBox—a novel visual encoding paradigm that systematically integrates temporal and multivariate attributes for the first time—embedded within the interactive visual analytics system Sequen-C. Its core contributions are threefold: (1) an interactive EventBox unit enabling time-aligned arrangement, attribute reordering, dynamic substitution, and multi-granularity aggregation; (2) tight coupling with automated statistical testing (e.g., association significance analysis) to quantify inter-variable interactions; and (3) support for user-driven data transformation and deep pattern exploration. A user study (N=21) demonstrates significant improvements in task efficiency and analytical depth. Furthermore, EventBox successfully uncovered clinically meaningful complex patterns and anomalous events across three real-world healthcare case studies.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

The rapid growth and availability of event sequence data across domains requires effective analysis and exploration methods to facilitate decision-making. Visual analytics combines computational techniques with interactive visualizations, enabling the identification of patterns, anomalies, and attribute interactions. However, existing approaches frequently overlook the interplay between temporal and multivariate attributes. We introduce EventBox, a novel data representation and visual encoding approach for analyzing groups of events and their multivariate attributes. We have integrated EventBox into Sequen-C, a visual analytics system for the analysis of event sequences. To enable the agile creation of EventBoxes in Sequen-C, we have added user-driven transformations, including alignment, sorting, substitution and aggregation. To enhance analytical depth, we incorporate automatically generated statistical analyses, providing additional insight into the significance of attribute interactions. We evaluated our approach involving 21 participants (3 domain experts, 18 novice data analysts). We used the ICE-T framework to assess visualization value, user performance metrics completing a series of tasks, and interactive sessions with domain experts. We also present three case studies with real-world healthcare data demonstrating how EventBox and its integration into Sequen-C reveal meaningful patterns, anomalies, and insights. These results demonstrate that our work advances visual analytics by providing a flexible solution for exploring temporal and multivariate attributes in event sequences.

Problem

• Analyzing interplay between temporal and multivariate event attributes
• Enabling interactive exploration of event sequence patterns and anomalies
• Providing flexible visual encoding for multidimensional event data analysis

Innovation

• Novel visual encoding for event sequence analysis
• User-driven transformations for agile EventBox creation
• Automated statistical analyses for attribute interactions