UP-FHDI: A Software for Big Incomplete Data Curing

Abstract

Fractional hot-deck imputation (FHDI) is a general-purpose, assumption-free imputation method for handling multivariate missing data by filling each missing item with multiple observed values without resorting to artificially created values. By leveraging FHDI theory and parallel computing techniques, ultra data-oriented parallel fractional hot-deck imputation (UP-FHDI) was proposed, capable of curing a wide spectrum of big missing data. However, strict pre-processing specifications, intricate imputation knowledge, and software deployment become major obstacles preventing UP-FHDI from real-world applications. To maximize users' benefits, we develop a graphical user interface (GUI) to enable fast and easy delivery of UP-FHDI. This paper elaborates on the GUI design process and novel features for improving user experience. Results affirm that the proposed software can tackle various synthetic and real-world incomplete datasets and seamlessly guide users for easy and quick deployment. UP-FHDI will benefit a broad audience in science and engineering without a strong background in coding and imputation.