Open AccessResearch on the Quality Evaluation Strategy of Multi-source Heterogeneous Aggregation Data
Author(s) -
Zhan Gaofeng,
Jiang Yong,
Xiaofeng Wang,
Yuyang Zhao,
Tong Zhao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012015
Subject(s) - computer science , multivariate statistics , quality (philosophy) , variance (accounting) , data mining , data quality , inference , data aggregator , perspective (graphical) , statistical inference , artificial intelligence , machine learning , statistics , mathematics , engineering , computer network , metric (unit) , philosophy , operations management , wireless sensor network , accounting , epistemology , business
In the era of big data, the application of multi-source heterogeneous aggregation data is more and more extensive. If the quality of aggregation data is uneven, it will bring a lot of troubles to the subsequent data mining, and then lead to inaccurate decision-making. A comprehensive quality evaluation method for aggregation data is proposed in this paper, based on factor analysis and multivariate variance analysis which is from the perspective of multivariate statistical inference. The case study shows that the method proposed in this paper is feasible and adaptive for the long-term evaluation of the quality of multi-source heterogeneous aggregation data.