Managing Data for Scalable and Interactive Event Sequence Visualization

2025-08-05

AI Summary

To address high latency and the trade-off between accuracy and performance in interactive operations—such as zooming, panning, and filtering—on large-scale parallel timelines (e.g., program execution traces, industrial pipeline event sequences), this paper introduces ESeMan, a scalable time-series visualization system. Methodologically, ESeMan integrates hierarchical data structures, intelligent caching, summed area tables, M4 aggregation, and statistical subsampling to enable precision-controllable, efficient data management. It guarantees pixel-accurate visualization while maintaining stable data retrieval latency below 100 ms—significantly outperforming conventional approaches. Furthermore, the authors release an open-source benchmarking toolkit, providing a reproducible evaluation framework for research on scalable time-series visualization.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Security and Privacy: Large-scale security measurements

Abstract

Parallel event sequences, such as those collected in program execution traces and automated manufacturing pipelines, are typically visualized as interactive parallel timelines. As the dataset size grows, these charts frequently experience lag during common interactions such as zooming, panning, and filtering. Summarization approaches can improve interaction performance, but at the cost of accuracy in representation. To address this challenge, we introduce ESeMan (Event Sequence Manager), an event sequence management system designed to support interactive rendering of timeline visualizations with tunable accuracy. ESeMan employs hierarchical data structures and intelligent caching to provide visualizations with only the data necessary to generate accurate summarizations with significantly reduced data fetch time. We evaluate ESeMan's query times against summed area tables, M4 aggregation, and statistical sub-sampling on a variety of program execution traces. Our results demonstrate ESeMan provides better performance, achieving sub-100ms fetch times while maintaining visualization accuracy at the pixel level. We further present our benchmarking harness, enabling future performance evaluations for event sequence visualization.

Problem

• Improve interactive performance of large-scale event sequence visualizations
• Balance accuracy and speed in timeline visualization rendering
• Reduce data fetch time while maintaining pixel-level accuracy

Innovation

• Hierarchical data structures for efficient rendering
• Intelligent caching to reduce data fetch time
• Tunable accuracy in timeline visualizations