III: Small: Statistical Learning Algorithms for Micro-Event Time Series Data

该项目的重点是开发新的数据分析工具，用于分析个人数据档案，即日常记录的反映个人日常生活不同方面的数字数据流。此类数据的示例包括博客、电子邮件历史记录、短信、社交媒体互动、微博以及身体活动、饮食和睡眠记录。随着传感器变得更加准确和便宜，并且随着数据存储变得有效地零成本，对允许个人分析和洞察自己的个人数据的数据分析工具的需求越来越大。该研究项目正在开发新的统计机器学习算法，用于分析这些类型的数据。该项目特别侧重于开发处理事件时间序列数据形式的个人档案的模型和算法，其中包括涉及与其他个人互动的有时间戳的事件日志以及文本和其他元数据。用于支持这项研究的测试数据集包括公开的电子邮件历史档案，软件开发讨论，Twitter微博，维基百科编辑交互和物理接近数据。就更广泛的社会影响而言，该项目正在开发的数据分析工具有可能显著改变个人分析个人数据的方式，以更好地了解和监测他们的身心健康。

This project is focused on the development of new data analysis tools for analyzing personal data archives, namely, the streams of digital data that are routinely recorded reflecting different aspects of individuals' daily lives. Examples of such data include keystrokes, email histories, text messages, social media interactions, microblogs, as well as records of physical activity, diet, and sleep. As sensors become more accurate and cheaper and as data storage becomes effectively zero cost, there is increasing demand for data analysis tools that allow individuals to analyze and gain insight into their own personal data. This research project is developing new statistical machine learning algorithms for analyzing these types of data. The project has a particular focus on the development of models and algorithms to handle personal archives in the form of event time-series data, consisting of logs of time-stamped events involving interactions with other individuals as well as textual and other metadata. Testbed data sets being used to support this research include publicly-available archives of email histories, software development discussions, Twitter microblogs, Wikipedia editing interactions, and physical proximity data. In terms of broader societal impact, the data analysis tools being developed by this project have the potential to significantly transform how individuals analyze their personal data to better understand and monitor their physical and mental health.