Managing Data for Scalable and Interactive Event Sequence Visualization

TL;DR

ESeMan is a system that enables scalable, accurate, and interactive visualization of large event sequences by using hierarchical data structures and caching to reduce data fetch times.

Contribution

The paper introduces ESeMan, a novel event sequence management system that improves interactive visualization performance while maintaining accuracy through hierarchical data structures and caching.

Findings

ESeMan achieves sub-100ms fetch times.

ESeMan maintains pixel-level visualization accuracy.

ESeMan outperforms existing aggregation and sampling methods.

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.