Sunghae  Jun, 
    A Statistical Method for Big Data with Excessive Zero-Inflated Problem,
    Journal of Engineering and Applied Sciences,
    Volume 14,Issue 8,
    2019,
    Pages 2465-2469,
    ISSN 1816-949x,
    jeasci.2019.2465.2469,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2019.2465.2469)
    Abstract: In many cases, we meet the zero-inflated problem in big data analysis. This is because the value of
zero is too much in the data table structured through preprocessing from collected big data. If the big data is
analyzed as it is the performances of estimation and prediction of statistical models will deteriorate. To build
valid models for big data analysis, we have to solve the zero-inflated problem of big data. So, we propose a
statistical modeling to overcome the zero-inflated problem in big data analysis. In this study, we combine the
method of data division with count data models such as Poisson, hurdle, negative binomial regressions. In
order to verify the validity of the proposed approach, we carry out case study using simulated and patent big
data.
    Keywords: Statistical model;big data;zero-inflated problem;count data analysis;patent big data analysis;validity;statistical modeling;data analysis