An Interactive Data Imputation System

Yangyang Wu, Xiaoye Miao, Yuchen Peng, Lu Chen, Yunjun Gao, Jianwei Yin

Abstract: In this demonstration, we propose an interactive data imputation system, called DITS, built upon ten state-of-the-art imputation algorithms and a novel variational generative adversarial imputation network. It consists of three modules, namely source uploader, algorithm evaluation, and interactive imputation. In the source uploader module, DITS allows users to register new imputation and prediction algorithms. Then, DITS is able to make users more aware of various integrated imputation algorithms via algorithm evaluation module, so as to support both lay and power users to operate a customized data imputation for their target dataset via the interactive imputation module. Using a public incomplete meteorologic dataset, we demonstrate that, DITS is capable of assisting users to effectively address real-life missing data issues.