In the last years the pervasive use of sensors, as they exist in smart devices, e.g., phones, watches, medical devices, has increased dramatically the availability of personal data. However, existing research on data collection primarily focuses on the objective view of reality, as provided, for instance, by sensors, often neglecting the integration of subjective human input, as provided, for instance, by user answers to questionnaires. This limits substantially the exploitability of the collected data. In this paper we present a methodology and a platform specifically designed for the collection of a combination of large-scale sensor data and qualitative human feedback. The methodology has been designed to be deployed on top, and enriches the functionalities of, an existing data collection APP, called iLog, which has been used in large scale, worldwide data collection experiments. The main goal is to put the key actors involved in an experiment, i.e., the researcher in charge, the participant, and iLog in better control of the experiment itself, thus enabling a much improved quality and richness of the data collected. The novel functionalities of the resulting platform are: (i) a time-wise representation of the situational context within which the data collection is performed, (ii) an explicit representation of the temporal context within which the data collection is performed, (iii) a calendar-based dashboard for the real-time monitoring of the data collection context(s), and, finally, (iv) a mechanism for the run-time revision of the data collection plan. The practicality and utility of the proposed functionalities are demonstrated by showing how they apply to a case study involving 350 University students.

翻译：近年来，随着智能设备（如手机、手表、医疗设备）中传感器的普及应用，个人数据的可用性急剧增加。然而，现有数据收集研究主要关注传感器所提供的客观现实视角，往往忽视了主观人类输入的整合，例如用户对问卷的回答。这极大地限制了所收集数据的可利用性。本文提出了一种专门用于收集大规模传感器数据与定性人类反馈相结合的方法论及平台。该方法论设计部署于现有数据收集应用iLog之上，并丰富了其功能；iLog已在全球范围内的大规模数据收集实验中得到应用。其主要目标是使实验涉及的关键参与者——即负责的研究人员、参与者及iLog系统——能更好地掌控实验过程，从而显著提升所收集数据的质量与丰富度。该平台的新功能包括：（i）数据收集所处情境上下文的时间维度表征，（ii）数据收集所处时间上下文的显式表征，（iii）基于日历的实时数据收集上下文监控仪表板，以及（iv）数据收集计划的运行时动态修订机制。通过将其应用于涉及350名大学生的案例研究，我们展示了所提功能模块的实用性与有效性。