A Process Pattern Model for Tackling and Improving Big Data Quality
通过一家大型电信公司的案例研究，开发了一个通用的过程模式模型，包含四种模式，帮助数据用户在不同情境下改进大数据质量。
Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality. The process pattern model is defined as a proven series of activities, aimed at improving the data quality given a certain context, a particular objective, and a specific set of initial conditions. Four different patterns are derived to deal with the variations in data quality of datasets. Instead of having to find the way to improve the quality of big data for each situation, the process model provides data users with generic patterns, which can be used as a reference model to improve big data quality.