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This study presents novel advances in the upscaling of operation datasets obtained from open-loop geothermal energy systems for an optimal integration in hydrogeological models.
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This laborious task requires better support from the computational framework being used, in the form of reliable ways to automate operations, process datasets in bulk and document the analysis performed on any of them.
To this purpose, a latent variable model is used to assess the consistency between the correlation structure of a historical operation dataset and that of a similar dataset generated using the FP model.
The consideration behind the approach is that large historical operation dataset are usually available in SGs and employed to extract useful information; besides, such datasets are also expected to grow over and over because of the pervasive deployment of SGs sensors.
The dataset POST operation allows uploading datasets in RDF representation, as well as files with chemical structures with arbitrary set of fields.
Top-k queries are desired aggregation operations on datasets.
After these operations, our dataset contained a total of 15,663 unique transcripts; accession numbers for these sequences are available in Additional file 5.
It can be a time-consuming operation on large datasets, converting in the order of 40 000 sequences per second, although this is only around 70% slower than converting SAM to BAM using SAMtools.
MR offers a compelling alternative for running tasks in a massively parallel way and it is successfully used by many well-known data-driven operations [ 16] to process huge datasets with sizes of up to terabytes [ 17] and even petabytes [ 18].
The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator.
Finally, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window specifications, providing concrete evidence of its significance.
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