A Multisource Retrospective Audit Method for Data Quality Optimization and Evaluation

With the rapid development of information technology and the coming of the era of big data, various data are constantly emerging and present the characteristics of autonomy and heterogeneity. How to optimize data quality and evaluate the effect has become a challenging problem. Firstly, a heterogeneous data integration model based on retrospective audit is proposed to locate the original data source and match the data. Secondly, in order to improve the integrated data quality, a retrospective audit model and associative audit rules are proposed to fix incomplete and incorrect data from multiple heterogeneous data sources. The heterogeneous data integration model based on retrospective audit is divided into four modules including original heterogeneous data, data structure, data processing, and data retrospective audit. At last, some assessment criteria such as redundancy, sparsity, and accuracy are defined to evaluate the effect of the optimized data quality. Experimental results show that the quality of the integrated data is significantly higher than the quality of the original data