Improving Data Quality and Data Analytics performance

More and more industrial activities rely on the analysis of huge amounts of data (Big Data). However, quite often data can be "noisy" (when data items are known to be incorrect and/or may have gaps), negatively affecting the performance of machine learning algorithms, and data analysis in general. The proposed research will provide novel techniques to achieve noise reduction and to reconstruct data points in very large collections of numerical data from sensorial sources, with proven guarantees (i.e. conservative estimation of missing data points, correction of data points within limits of neighbouring data points, and so on). The research uses data collections and scenarios from the Oil & Gas sector, but the techniques and results are of far wider importance, benefiting for instance health, finance and other sectors. The research aims to provide better data quality and drastically improve the performance of data interpretation.