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In the literature on sensing and monitoring, missing data is often treated as a nuisance. Statisticians have investigated many ways of filling gaps in a data set, depending on whether the data is missing completely randomly, whether the patterns of missing data can be predicted from other variables in the data set, or whether the patterns of missing data are regular, but hard to predict from existing data.
In this talk, I argue that missing data is evidence for how people engage with sensors and devices. I outline the processes that result in the absence of data, and discuss how this contextual understanding can be used to improve interpretation and analytics.
This talk is based on joint work with the Help4Mood team (http://help4mood.info) and Henry Potts, UCL, and Katarzyna Stawarz, Bristol, during an Alan Turing Institute Summer Programme Small Group.