Abstract
Computer systems designed to help user in their daily activities are becoming a norm. Specially, with the advent of the Internet of Things (IoT) where every device is interconnected with others through internet based protocols, the amount of data and information available has increased. Tracking devices are targeting more and more activities such as fitness, utilities consumption, movement, environment state, weather. Nowadays, a challenge for researchers is to handle such income of data and transform it into meaningful knowledge that can be used to predict, foresight, adapt and control activities. In order to this, it is necessary to interpret contextual information and produce services to anticipate these conditions. This project aim to provide a system for the creation of information and data structures to generate user models based on activity and sensor based contextual-information from IoT devices and apply machine learning operations to anticipate future states.
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Silva, F., Analide, C. (2016). Context Time-Sequencing for Machine Learning and Sustainability Optimization. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds) Intelligent Distributed Computing IX. Studies in Computational Intelligence, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-25017-5_29
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DOI: https://doi.org/10.1007/978-3-319-25017-5_29
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