HDQGF: Heterogeneous Data Quality Guarantee Framework Based on Deep Learning

Yun Zhang, Zongze Jin, Weilin Zhu, Lei Chi, Weiping Wang

Abstract: Although user-generated data on the Internet contains rich information, many approaches cannot effectively guarantee data quality for data analysis from raw data. In recent years, many researches on data quality guarantee using machine learning have shown that enhancing data quality is conducive to improve the accuracy of analysis results. But the existing approaches only consider the single dimension and neglect the fusion of heterogeneous data, such as images or social graphs. To consider this element and address the above issue, we leverage the deep learning technique to guarantee the data quality by using the heterogeneous data. Our framework is named HDQGF, which is an end-to-end approach using a combination of multiple networks to fuse heterogeneous information. In order to verify the effectiveness of the model, we designed related experiments on three real datasets. According to the experimental results, our model HDQGF can enhance the performance by improving the data quality.