Selected Algorithms and designs Sample Clauses

Selected Algorithms and designs. The selected algorithms address the QualiMaster goals to process big data and streaming data from wide range of financial and social networks sources. This selection has been shown in D3.1 where the Count Min Sketch and its Exponential Histogram extension to create Histograms for big streaming data have been mapped with very promising results. Also, the Xxxxxxx - Xxxxxxx correlation estimator has been mapped to hardware with simulated capacity for real time correlation estimation 10 times larger than software. The hardware design was experimentally verified to calculate the correlation of 5,000 pairs of stocks instead of the 500 pairsthat the software can calculate – a significant result since the one order of magnitude change corresponds to two orders of magnitude more computations (the problem scales with O(n2)). The Support Vector Machine (SVM) training algorithm has also been mapped to hardware, with the implementation focusing on social network data processing (e.g. dimensioning the problem, using high accuracy, etc.). It was the first full FPGA implementation of SVM training which has the same accuracy as the well-known libSVM package to our knowledge. Results proved not to be satisfying due to Platform design restriction and I/O bounds. In order to process social network and financial data two new algorithms have been selected to be implemented for the project. These are the Mutual Information estimator and the Transfer Entropy estimator. Both algorithms can be used for the statistical analysis of financial and social network data. These algorithms are presented in the present deliverable, and they are giving promising initial results (presented in the deliverable). Both algorithms were mapped in hardware by following the methodology proposed in D3.1 and the initial results show speedups of 10 times vs. the optimized software [24].
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