论文标题
通过个人大数据收集来改善时间使用测量 - 欧洲大数据hackathon 2019的体验
Improving time use measurement with personal big data collection -- the experience of the European Big Data Hackathon 2019
论文作者
论文摘要
本文评估了欧洲统计技术和统计技术（NTTS）会议的卫星事件，评估了I-DATA HACKATHON 2019的I-LOG经验，该活动由Eurostat组织。 I-LOG是一个系统，它允许从智能手机的内部传感器中捕获个人大数据，以用于时间使用测量。它允许收集异质类型的数据，从而为社会学城市现场研究提供了新的可能性。传感器数据（例如与位置或用户的移动相关的数据）可用于调查并了解时间日记的答案并评估其整体质量。关键的想法是，用户的答案用于训练机器学习算法，使系统可以从用户的习惯中学习并生成新的时间日记的答案。反过来，这些新标签可用于评估现有标签的质量，或者在用户不提供答案时填补空白。本文的目的是介绍试点研究，I-LOG系统以及在调查过程中产生的方法学证据。
This article assesses the experience with i-Log at the European Big Data Hackathon 2019, a satellite event of the New Techniques and Technologies for Statistics (NTTS) conference, organised by Eurostat. i-Log is a system that allows to capture personal big data from smartphones' internal sensors to be used for time use measurement. It allows the collection of heterogeneous types of data, enabling new possibilities for sociological urban field studies. Sensor data such as those related to the location or the movements of the user can be used to investigate and have insights on the time diaries' answers and assess their overall quality. The key idea is that the users' answers are used to train machine-learning algorithms, allowing the system to learn from the user's habits and to generate new time diaries' answers. In turn, these new labels can be used to assess the quality of existing ones, or to fill the gaps when the user does not provide an answer. The aim of this paper is to introduce the pilot study, the i-Log system and the methodological evidence that arose during the survey.
