事件序列分析的多层次任务框架

Kazi Tasnim Zinat;Saimadhav Naga Sakhamuri;Aaron Sun Chen;Zhicheng Liu
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摘要

尽管针对不同领域(包括但不限于医疗保健、数字营销和用户行为分析)的事件序列数据开发了大量可视化分析工具,但比较这些特定领域的研究并将结果转移到新的数据集和问题领域仍具有挑战性。任务抽象可以帮助我们超越特定领域的细节,但现有的可视化任务抽象不足以进行事件序列可视化分析,因为它们主要侧重于多变量数据集,往往忽略了自动分析技术。为了弥补这一不足,我们提出了一个用于事件序列分析的领域区分型多层次任务框架,该框架源自对 58 篇介绍事件序列可视化系统的论文的分析。我们的框架包括四个层次:目标、意图、策略和技术。总体目标确定了分析的主要目标。意图包括在每个分析步骤中采用的五种高级方法:增强数据、简化数据、配置数据、配置可视化和管理出处。每个意图都可以通过多种策略来实现,例如,数据简化可以通过聚合、汇总或分割来实现。最后,根据输入和输出组件的不同,每种策略都可以通过一系列技术来实现。我们进一步证明,每种技术都可以通过 "行动-输入-输出-标准 "四元组来表达。我们通过案例研究展示了该框架的描述能力,并讨论了它与之前的事件序列任务分类法的异同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Level Task Framework for Event Sequence Analysis
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.
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