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The challenge lies in determining how to provide a rich and detailed 3D virtual environment to these thin devices, since they are known for their lack of the proper resources required to process large scale 3D geometric data.
From the ICT perspective, the possibility of realisation of smart cities is being enabled by smarter hardware and software e.g. IoTs i.e. RFIDs, smart phones, sensor nets, smart household appliances, and capacity to manage and process large scale data using cloud computing without compromising data security and citizens privacy [16].
Given the background and incentives above, we have developed several user-defined functions (UDF) to process large scale administrative data for ease of epidemiological analysis, based on a Pig Latin script in the Hadoop framework [ 17].
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The designed parallel algorithm is demonstrated to be able to process larger scale of data and the new analysis method is practical.
It is challenging for existing geodesic path algorithms to process large-scale models with millions of vertices.
Besides, in order to have a reliable performance evaluation process, large-scale instances are regarded in computational analysis.
In the recent years, these fields have begun to intertwine, and it is increasingly becoming clear that we need to make use of multidisciplinary research to better process large-scale visual data.
Now, in the production process, large-scale data with more helpful information are sampled, which provides possibilities to improve the precision of the temperature prediction.
A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.
The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.
Currently, IoT platforms provide intelligent services; however, most of these services simply process large-scale data and handle mathematical calculations more quickly and accurately than a person.
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