Improving Data Quality and Data Analytics performance

提高数据质量和数据分析性能

• 批准号: 1957361
• 金额: --
• 依托单位: University of Aberdeen
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1957361
• 关键词: Improving; Data; Quality; 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.

越来越多的工业活动依赖于对海量数据的分析（大数据）。然而，通常数据可能是“嘈杂的”（当数据项已知是不正确的和/或可能存在差距时），这会对机器学习算法的性能和数据分析产生负面影响。拟议的研究将提供新的技术来实现降噪和重建来自感官来源的大量数值数据中的数据点，并具有经过验证的保证（即对缺失数据点的保守估计，在邻近数据点的范围内对数据点进行校正，等等）。该研究使用了石油和天然气行业的数据收集和场景，但其技术和结果的重要性要广泛得多，可以使卫生、金融和其他行业受益。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。

项目成果

Towards a context-dependent numerical data quality evaluation framework

建立依赖于上下文的数值数据质量评估框架

• DOI: 10.48550/arxiv.1810.09399
• 发表时间: 2018
• 期刊: arXiv e-prints
• 作者: Marev Milen S.