Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science
Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science
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DOI：
10.1089/big.2015.0049
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发表时间：
2015-12-01
期刊：
影响因子：
4.6
通讯作者：
Fawcett, Tom
中科院分区：
文献类型：
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作者：
Fawcett, Tom
在过去的几年里，人们对可穿戴计算、个人跟踪设备和所谓的量化自我（QS）运动的兴趣激增。量化自我涉及普通人记录和分析他们生活的许多方面，以了解和提高自己。这现在是一个主流现象，吸引了大量的关注、参与和资金。随着越来越多的人被这项运动所吸引，公司正在提供各种新的平台（硬件和软件），允许跟踪日常生活的更多方面。QS生态系统的几乎每个方面都在快速发展，除了分析能力，这仍然是令人惊讶的原始。随着越来越多的合格的自我参与者收集越来越多的数据和类型，许多人实际上拥有的数据比他们知道如何处理的数据还要多。本文回顾了QS运动带来的机遇和挑战。数据科学为知识发现提供了久经考验的技术。但是，使这些对QS领域有用提出了独特的挑战，这些挑战来自收集的数据的特征以及人们希望从数据中获得的具体类型的可操作见解。使用一个小样本的QS时间序列数据包含有关个人健康的信息，我们提供了一个配方的QS问题，连接数据的决定感兴趣的用户。
The last several years have seen an explosion of interest in wearable computing, personal tracking devices, and the so-called quantified self (QS) movement. Quantified self involves ordinary people recording and analyzing numerous aspects of their lives to understand and improve themselves. This is now a mainstream phenomenon, attracting a great deal of attention, participation, and funding. As more people are attracted to the movement, companies are offering various new platforms (hardware and software) that allow ever more aspects of daily life to be tracked. Nearly every aspect of the QS ecosystem is advancing rapidly, except for analytic capabilities, which remain surprisingly primitive. With increasing numbers of qualified self participants collecting ever greater amounts and types of data, many people literally have more data than they know what to do with. This article reviews the opportunities and challenges posed by the QS movement. Data science provides well-tested techniques for knowledge discovery. But making these useful for the QS domain poses unique challenges that derive from the characteristics of the data collected as well as the specific types of actionable insights that people want from the data. Using a small sample of QS time series data containing information about personal health we provide a formulation of the QS problem that connects data to the decisions of interest to the user.