Abstract
Analysis of training data has become an inseparable part of sports preparation not only for professional athletes but also for sports enthusiasts and sports amateurs. Nowadays, smart wearables and IoT devices allow monitoring of various parameters of our physiology and activity. The intensity and effectiveness of the activity and values of some physiology parameters may depend on weather conditions in particular days. Therefore, for efficient analysis of training data, it is important to align training data to weather sensor data. In this paper, we show how this process can be performed with the use of the fuzzy join technique, which allows to combine data points shifted in time.
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Acknowledgements
This work was supported by Microsoft Research within Microsoft Azure for Research Award grant, pro-quality grant for highly scored publications or issued patents of the Rector of the Silesian University of Technology, Gliwice, Poland (grant No 02/020/RGJ19/0167), and partially, by Statutory Research funds of Institute of Informatics, Silesian University of Technology, Gliwice, Poland (grant No BK/204/RAU2/2019).
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Wachowicz, A., Mrozek, D. (2019). Fuzzy Join as a Preparation Step for the Analysis of Training Data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis. BDAS 2019. Communications in Computer and Information Science, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-030-19093-4_20
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