The Tour d’ Horizon of Data Law Implications of Digital Twins
efficiently, improve building sustainability, and map potential solar projects. 16 Additionally, Virtual Singapore would also be categorized as a Community DT( explained above) providing insights into day-to-day operations.
2 DATA FLOWS
DTs are data intensive and process large volumes of non-personal and personal data which is transmitted from the physical asset. A common concern across all applications is the heavy reliance of DT on input and real time data. All factors of performance of the DT including efficacy, capacity, replications and implementation are directly co-related to the quality and quantity of data of the original application which is made available to the DT(“ Input Data”) which flows from the physical assets which the DT replicates. Given the significance of such Input Data, this section delineates the key stages of data flow, with a particular emphasis on data acquisition, recognizing its pivotal role in the analysis set forth in this article.
2.1 ACQUISITION
In most DT models, the data flows directly from the physical asset on a real time basis or is procured from the physical asset. Depending on the type of the DT application, appropriate quality Input Data is identified. For example, where a replica of a human organ is the DT application, the Input Data is likely to be personal data( identification of the individual, medical records, physiological data, genetic data). Such data may be procured either directly from the individual or through a healthcare provider. On the other hand, in case of engine DT, the Input Data may be procured directly through the IoT sensors attached to the physical engine. One of the primary considerations for the smooth functioning of the DT model is to ensure that the Input Data is procured from an identified source in accordance with applicable laws governing data collection, sharing and processing.
2.2 VALIDATION, STRUCTURING AND ANONYMIZATION
Accuracy and quality of data is vital, especially when sourced from multiple origins. Without proper validation and filtration of procured data; erroneous or biased data may negatively impact the simulations and predictions of the DT. Subsequent to collection, the data should be validated and verified; and classified and categorized in alignment with the purpose of use. The collected data may also be anonymized based on the requirement of the operations sought to be conducted. There may be different processes and mechanisms adopted for this process depending upon the DT application and its utility.
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Digital Twinning a Country, Singapore, World Geospatial Industry Council. https:// wgicouncil. org / digital-twinning-a-country-singapore /
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