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All images were iteratively reconstructed using post-emission transmission attenuation-corrected datasets (size 168; zoom 1; full width at half maximum (FHWM) 5.0 mm; iterations 4; subsets 8).
The choice of the datasets size was made to guarantee a conservative evaluation, by providing negative information a greater weight.
As datasets size increases (i.e. >10), algorithms that directly make use of pairwise distance or similarity information become infeasible as they inevitably exhibit a quadratic complexity.
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BS= 5% of dataset size.
First, performance is related to dataset size.
Metrics on computational time versus dataset size are also presented.
We also plan to continue increasing our dataset size.
We have found a positive correlation between dataset size and performance.
The buffer size BS is set to 5% of the dataset size.
The results have been analysed based on various sizes of datasets, better results have been achieved with increasing dataset size.
Flink exhibits better scalability, especially after 90 M records of input dataset size.
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