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CPD 2018: The First Workshop on Combining Physical and Data-Driven Knowledge in Ubiquitous Computing

Real-world ubiquitous computing systems face the challenge of requiring a significant amount of data to obtain accurate information through pure data-driven approaches. The performance of these data-driven systems greatly depends on the quantity and 'quality' of data. In ideal conditions, pure data-driven methods perform well due to the abundance of data. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. Physical knowledge, on the other hand, can be used to alleviate these issues of data limitation. This physical knowledge can include domain knowledge from experts, heuristics from experiences, as well as analytic models of the physical phenomena. This workshop aims to explore the intersection between (and the combination of) data and physical knowledge. The workshop will bring together domain experts that explore the physical understanding of the data, practitioners that develop systems and the researchers in traditional data-driven domains. The workshop welcomes addressing these issues in different applications/domains as well as algorithmic and systematic approaches to applying physical knowledge. Therefore, we further seek to develop a community that systematically analyzes the data quality regarding inference and evaluates the improvements from the physical knowledge. Preliminary and on-going work is welcomed.