Observational data gathering is expensive as it traditionally relies on human intervention and intuition. However, with advances in Artificial Intelligence (AI) machines are gaining the capacity in making sense of the unstructured data observational methods yield [1]. With these advances in technology there is a new push to create innovative ways of gathering observational data to give context to self-reported user feedback.
CITATION STYLE
Ebeling, D., Luker, Z., Pacheco, S., Payne, A., & Rae, N. (2018). Validating self-reported trends using WiFi tracking. In Communications in Computer and Information Science (Vol. 852, pp. 233–237). Springer Verlag. https://doi.org/10.1007/978-3-319-92285-0_32
Mendeley helps you to discover research relevant for your work.