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It integrates actively acquired, behaviourally relevant observational data (e.g. face-to-face/internet-based stated preference) with innovative passive data collection methodologies, based on pervasive/low-cost sensing technologies (e.g. GPS and smartphones), producing unprecedented high quality and quantity datasets for model estimation and validation (e.g. [43, 44, 45]). 2.
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Smart grid systems collect huge quantity of datasets to bring smartness to the gird.
Big Data can be defined as a huge quantity of datasets, but in fact it includes other features.
The grid collects data from different sources and stores it as a huge quantity of dataset that should be easily consumable for analytics.
Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown.
As the quality and quantity of such datasets supports more or less complex modeling approaches, we consider both stochastic differential equations (SDEs) and ordinary differential equations (ODEs) with measurement noise.
We store the mismatch tally in a dataset called Counts and further quantities in the datasets Coverages, Deletions and Reference.
Some of this vagueness could be reduced by adding some specification of quantity to the dataset, i.e. how many, and who, hold which enablers as important.
The ACT site contains downloadable code in a variety of formats, interactive web servers (for use on small quantities of data), example datasets, documentation and a gallery of outputs.
The solutions found in literature are classified according to three different situations from engineering practice: 1) completing an incomplete dataset about the quantity and quality of the influent wastewater; 2) translating the common quality measurements (COD, TSS, TKN, etc).
However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets.
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