Improving Data Quality and Data Analytics performance

越来越多的工业活动依赖于对大量数据（大数据）的分析。然而，很多时候，数据可能是“嘈杂的”（当数据项已知是不正确的和/或可能有差距），对机器学习算法的性能和一般的数据分析产生负面影响。拟议的研究将提供新的技术，以实现降噪和重建数据点在非常大的数值数据集合从感官来源，与证明的保证（即保守估计丢失的数据点，校正的数据点的限制内的相邻数据点，等等）。该研究使用了石油和天然气部门的数据收集和情景，但技术和结果具有更广泛的重要性，例如卫生，金融和其他部门。该研究旨在提供更好的数据质量，并大大提高数据解释的性能。

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.