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@inproceedings{1802049, author = {Blanco Sánchez, José Miguel and Ge, Mouzhi and Pitner, Tomáš}, address = {Szczecin}, booktitle = {Procedia Computer Science, Volume 192, 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021}, doi = {http://dx.doi.org/10.1016/j.procs.2021.08.130}, keywords = {Web of Things Internet of Things Semantic Web Reasoners}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Szczecin}, pages = {1265-1273}, publisher = {Elsevier}, title = {Modeling Inconsistent Data for Reasoners in Web of Things}, year = {2021} }
TY - JOUR ID - 1802049 AU - Blanco Sánchez, José Miguel - Ge, Mouzhi - Pitner, Tomáš PY - 2021 TI - Modeling Inconsistent Data for Reasoners in Web of Things PB - Elsevier CY - Szczecin KW - Web of Things Internet of Things Semantic Web Reasoners N2 - With the recent developments of the Internet of Things and its integration in the web environment, the Web of Things and the real-time data submissions to Reasoners are enabled. However, the data that are fed to the Reasoners are often inconsistent. This can be possibly caused by the malfunction of certain Internet of Things device or by human errors. The data consistency issue is becoming more complex in the Web of Things network. This paper, therefore, proposes a new data processing model to tackle the inconsistent data, so that the processed data can be further used in Reasoners. The data processing model introduces an oversimplification of the Shramko-Wansing sixteen-valued trilattice, which is an extension of Belnap’s four-valued bilattice to assign the data classical truth-values. A preliminary implementation is demonstrated to validate the proposed model. The result shows that our model can avoid system collapse when contradictory outputs exist. ER -
BLANCO SÁNCHEZ, José Miguel, Mouzhi GE a Tomáš PITNER. Modeling Inconsistent Data for Reasoners in Web of Things. Online. In \textit{Procedia Computer Science, Volume 192, 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021}. Szczecin: Elsevier, 2021, s.~1265-1273. ISSN~1877-0509. Dostupné z: https://dx.doi.org/10.1016/j.procs.2021.08.130.
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