Abstract
In recent years, the usage of electronic health records (EHR), wearable devices, and health applications has expanded in popularity. Because of the abundance of data that has been accumulated and integrated, health self-management is becoming more practicable. Some of the difficulties that the current healthcare system is facing include smart homes and smart workplaces enabled by the internet of things. The Web of Things (WoT) is a subset of the Internet of Things that aims to connect everyday things to the Internet and manage interoperability. Furthermore, collaboration of health data with data from various devices at home and at work, as well as open data on the Internet, is critical for successful and accessible health self-management. Unfortunately, shared health data may be untrustworthy for a variety of reasons. Uncertainty can be caused by heterogeneity, incompleteness, unavailability, and data inconsistency. To address the problem of health data uncertainty, we provide a probabilistic approach for composing uncertain Health Connected Data and computing the probabilities to deliver the final degree of uncertainty. We also present a method for parsing typical WoT objects into a new programmatic form that mimics the uncertainty of health data. Using a health care use case, we show how our technique successfully integrates uncertain health data with home, work, and sport environment data for the WoT domain.
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Boulaares, S., Sassi, S., Benslimane, D., Faiz, S. (2022). Uncertain Integration and Composition Approach of Data from Heterogeneous WoT Health Services. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13376. Springer, Cham. https://doi.org/10.1007/978-3-031-10450-3_13
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