Visual Analytics of Complex Event Sequence Data

事件序列对一段时间内发生的一系列有序事件进行建模。这类序列在广泛的应用中发挥着重要作用。例如，电子健康记录（EHRs）包含在临床过程中记录的患者的带有时间戳的医疗事件（例如，诊断、实验室测试）。另一个例子是，典型的软件开发过程包括一系列事件，比如提交到存储库。类似地，图形界面的经验评估可以记录用户行为，如凝视、打字或鼠标点击，这些行为也可以通过事件序列建模。在这个项目中，我们的目标是开发新的分析和可视化技术，以支持用户在不同的应用领域获得对事件序列数据的见解。我们如何从大量的事件序列中发现事件模式，以便人们能够找到潜在的规则来帮助解释序列中的因果关系，甚至预测未来的事件？不幸的是，现实世界的事件序列通常规模很大，事件类型多样，序列长度不同，事件可能以不同的顺序发生，持续的时间也不同，因此很难总结事件模式。我们计划通过创建一种新的可视化分析方法来全面分析复杂的事件序列数据，该方法适应并扩展了数据挖掘和机器学习技术，将它们集成到交互式可视化分析管道中。具体来说，我们将首先开发预处理和清理复杂序列数据的技术，以便估计事件的重要性，根据底层语义将长序列分成片段，对齐不同长度的序列，并从输入数据中去除噪声。之后，我们将开发一种面向知识的表示学习技术，将各种类型的事件根据其语义关系转换为统一的特征表示。利用得到的特征表示，我们将专注于多尺度序列总结、事件因果分析、事件预测和异常检测的可视化分析技术。我们将把数据预处理、分析和可视化技术集成到事件序列数据的可视化分析工具包中。最后，我们将把我们的技术应用于广泛的重要场景：电子病历的新数据分析方法、经验用户研究和软件工程中的事件序列。总的来说，我们期待基础研究的新结果，这将促进序列数据的可视化分析以及三个应用实例的实际改进。

Event sequences model an ordered series of events that have occurred over a period of time. Such sequences play an important role in a wide range of applications. For instance, electronic health records (EHRs) contain time-stamped medical events (e.g., diagnoses, lab tests) for patients recorded over the course of a clinical process. As another example, a typical software development process includes a series of events such as commits to a repository. Similarly, the empirical evaluation of graphical interfaces may record user behaviors such as gazing, typing, or mouse clicking, which can also be modeled by event sequences. In this project, we aim to develop novel analysis and visualization techniques to support users in obtaining insights into event sequence data in different application domains. How can we discover event patterns from a large collection of event sequences so that people can find the underlying rules to help interpret causality within a sequence and even predict future events? Unfortunately, real-world event sequences are usually large in scale, diverse in event type, vary in sequence length, and events may occur in different orders and last for different durations, thus making the summarization of event patterns difficult. We plan to comprehensively analyze complex event sequence data by creating a novel visual analytics method that adapts and extends data mining and machine learning techniques, integrating them into an interactive visual analysis pipeline.Specifically, we will first develop techniques for preprocessing and cleansing complex sequence data in order to estimate the importance of events, split long sequences into segments based on the underlying semantics, align sequences of different lengths, and remove noise from input data. After that, we will develop a knowledge-oriented representation learning technique that transforms various types of events into a uniform feature representation based on their semantic relations. Utilizing the resulting feature representations, we will then focus on visual analytics techniques for multiscale sequence summarization, event causality analysis, event prediction, and anomaly detection. We will integrate the data preprocessing, analysis, and visualization techniques into a visual analytics toolkit for event sequence data. Finally, we will apply our techniques to a wide range of important scenarios: novel data analysis methods for EHRs, empirical user studies, and event sequences in software engineering. Overall, we expect novel results for basic research that will advance visual analytics of sequence data as well as practical improvements for the three application examples.