Digital Twin Modeling of Smart Cities
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Smart cities utilize the Big Data and IoT to provide better life for citizens. Since, they are the most complicated human artifact, the adoption of such technologies become a complex task, requiring continuous data collection, aggregation and analysis. In order to transform city problems into concrete actions a systematic approach aimed at digital transition needs to be followed. There are huge efforts to build city information models for encoding city objects, their relations and supporting the decision-making. This requires a common knowledge base, supported by rich vocabularies and ontologies that are capable to handle the information diversity and overload.
In this paper a methodological framework and an upper-level ontology for building digital city models are presented. The process of digital city modelling follows the concept of digital twin by providing a data-driven decision making. The proposed upper-level ontology aims to overcome city modeling problems due to data silos and lack of semantic interoperability.
KeywordsData-driven decision making Digital twin Methodological framework Smart city Upper-level city ontology
This research work has been supported by GATE project, funded by the Horizon 2020 WIDESPREAD-2018-2020 TEAMING Phase 2 programme under grant agreement no. 857155 and Big4Smart project, funded by the Bulgarian National Science fund, under agreement no. DN12/9.
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