Extracting Structured Knowledge From Sensor Data for Hybrid Simulation

Ortolani, Marco

Abstract

Obtaining continuous and detailed monitoring of indoor environments has today become viable, also thanks to the widespread availability of effective and flexible sensing technology; this paves the way for the design of practical Ambient Intelligence systems, and for their actual deployment in real-life contexts, which require advanced functionalities, such as for instance the automatic discovery of the activities carried on by users. Novel issues arise in this context; on one hand, it is important to reliably model the phenomena under observation even though, to this end, it is often necessary to craft a carefully designed prototype in order to test and fine-tune the theoretical models.

Citation

Ortolani, M. (2014). Extracting Structured Knowledge From Sensor Data for Hybrid Simulation. In Advances onto the Internet of Things (153-165). Springer. https://doi.org/10.1007/978-3-319-03992-3_11

• Online Publication Date: Dec 31, 2014
• Publication Date: Jan 15, 2014
• Deposit Date: Dec 14, 2023
• Publisher: Springer
• Pages: 153-165
• Series Title: Advances in Intelligent Systems and Computing
• Series ISSN: 2194-5357; 2194-5365
• Book Title: Advances onto the Internet of Things
• Chapter Number: 11
• ISBN: 9783319039916; 9783319039923
• DOI: https://doi.org/10.1007/978-3-319-03992-3_11
• Publisher URL: Springer