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In addition, due to multidisciplinary nature of smart city application domains, engagement with domain experts is needed to identify basic relationships and dependencies between different data elements.
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They may capture data using different instruments, different databases, different data architectures, different data elements and immensely different formats.
The data used in this study come from multiple sources with different data elements and quality.
We designed several models that describe the different data elements, describe their relationships and hold the data.
For the METABRIC study we developed 79 CDEs that correspond to 33 different data element concepts.
This work presents a general approach, coined "Spy Element Method" (SEM), for parallelising workloads to run on manycore processors and featuring dynamic dependencies between different data items, such as graph traversals, remeshing methods and particle simulations.
Particularly important are the relationships between different data points.
For SAIL to function effectively across multiple data sources, it is important that their vocabularies are compatible and that the correct relations are made between data elements in different vocabularies.
Data clustering, one of the most important techniques in data mining, aims to group unlabeled data into different groups on the basis of similarities and dissimilarities between the data elements.
Moreover, between the different data sources, the relationships between data score and co-complexness are different.
The data elements in the web thus represent real-world entities and the links between data elements embody the logical relations between those entities.
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