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A Multi-Level Task Framework for Event Sequence Analysis

Kazi Tasnim Zinat, Saimadhav Naga Sakhamuri, Aaron Sun Chen, Zhicheng Liu

TL;DR

This paper tackles the challenge of transferring insights across domains in event sequence visual analytics by introducing a domain-agnostic, four-level task framework (objective, intent, strategy, technique) derived from an analysis of 58 papers. It defines techniques using a four-part action-input-output-criteria scheme and situates these within a hierarchical structure that links end-to-end data preprocessing, analysis, visualization, and provenance tasks. The framework is developed via corpus assembly, open coding, and axial coding, and is validated through three case studies and comparisons with existing taxonomies, demonstrating superior expressiveness for end-to-end workflows and low-level actions. The authors discuss descriptive, evaluative, and generative powers, highlighting how the framework supports knowledge transfer, tool evaluation, and design guidance, while acknowledging limitations and outlining future work toward formal specifications and executable benchmarks.

Abstract

Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific investigations and transferring the results to new datasets and problem areas remain challenging. Task abstractions can help us go beyond domain-specific details, but existing visualization task abstractions are insufficient for event sequence visual analytics because they primarily focus on multivariate datasets and often overlook automated analytical techniques. To address this gap, we propose a domain-agnostic multi-level task framework for event sequence analytics, derived from an analysis of 58 papers that present event sequence visualization systems. Our framework consists of four levels: objective, intent, strategy, and technique. Overall objectives identify the main goals of analysis. Intents comprises five high-level approaches adopted at each analysis step: augment data, simplify data, configure data, configure visualization, and manage provenance. Each intent is accomplished through a number of strategies, for instance, data simplification can be achieved through aggregation, summarization, or segmentation. Finally, each strategy can be implemented by a set of techniques depending on the input and output components. We further show that each technique can be expressed through a quartet of action-input-output-criteria. We demonstrate the framework's descriptive power through case studies and discuss its similarities and differences with previous event sequence task taxonomies.

A Multi-Level Task Framework for Event Sequence Analysis

TL;DR

This paper tackles the challenge of transferring insights across domains in event sequence visual analytics by introducing a domain-agnostic, four-level task framework (objective, intent, strategy, technique) derived from an analysis of 58 papers. It defines techniques using a four-part action-input-output-criteria scheme and situates these within a hierarchical structure that links end-to-end data preprocessing, analysis, visualization, and provenance tasks. The framework is developed via corpus assembly, open coding, and axial coding, and is validated through three case studies and comparisons with existing taxonomies, demonstrating superior expressiveness for end-to-end workflows and low-level actions. The authors discuss descriptive, evaluative, and generative powers, highlighting how the framework supports knowledge transfer, tool evaluation, and design guidance, while acknowledging limitations and outlining future work toward formal specifications and executable benchmarks.

Abstract

Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific investigations and transferring the results to new datasets and problem areas remain challenging. Task abstractions can help us go beyond domain-specific details, but existing visualization task abstractions are insufficient for event sequence visual analytics because they primarily focus on multivariate datasets and often overlook automated analytical techniques. To address this gap, we propose a domain-agnostic multi-level task framework for event sequence analytics, derived from an analysis of 58 papers that present event sequence visualization systems. Our framework consists of four levels: objective, intent, strategy, and technique. Overall objectives identify the main goals of analysis. Intents comprises five high-level approaches adopted at each analysis step: augment data, simplify data, configure data, configure visualization, and manage provenance. Each intent is accomplished through a number of strategies, for instance, data simplification can be achieved through aggregation, summarization, or segmentation. Finally, each strategy can be implemented by a set of techniques depending on the input and output components. We further show that each technique can be expressed through a quartet of action-input-output-criteria. We demonstrate the framework's descriptive power through case studies and discuss its similarities and differences with previous event sequence task taxonomies.
Paper Structure (36 sections, 1 figure, 2 tables)

This paper contains 36 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Task Abstractions for Visualizations
  4. Surveys and Taxonomies for Event Sequence Visual Analysis
  5. Method
  6. Corpus Assembly
  7. Open Coding
  8. Axial Coding
  9. Framework
  10. Augment Data
  11. DERIVE
  12. ORGANIZE
  13. SIMULATE
  14. Simplify Data
  15. AGGREGATE
  16. ...and 21 more sections

Figures (1)

  • Figure 1: Our multi-level task framework consists of four hierarchical levels: objectives, intents, strategies, and techniques. We identify six overarching objectives of event sequence analysis. Each analysis step is associated with one of the five intents, depicting the purpose of the analysis step. Intents are realized through multiple strategies. Finally, a wide range of techniques are available for implementing each strategy.