Abstract “Big data” is a buzzword these days due to an enormous amount of data-rich applications in different industries and research projects. In practice, big data often take the form of data streams in the sense that new batches of data keep being collected over time. One fundamental research problem when analyzing big data in a given application is to monitor the underlying sequential process of the observed data to see whether it is longitudinally stable, or how its distribution changes over time. To monitor a sequential process, one major statistical tool is the statistical process control (SPC) charts, which have been developed and used mainly for monitoring production lines in the manufacturing industries during the past several decades. With many new and versatile SPC methods developed in the recent research, it is our belief that SPC can become a powerful tool for handling many big data applications that are beyond the production line monitoring. In this article, we introduce some recent SPC methods, and discuss their potential to solve some big data problems. Certain challenges in the interface between the current SPC research and some big data applications are also discussed.

中文翻译：

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大数据？统计过程控制可以提供帮助！

摘要 由于不同行业和研究项目中大量数据丰富的应用程序，“大数据”是当今的流行词。在实践中，大数据通常采用数据流的形式，即随着时间的推移不断收集新批次的数据。在给定应用程序中分析大数据时，一个基本的研究问题是监视观察数据的潜在顺序过程，以查看它是否纵向稳定，或其分布如何随时间变化。为了监控顺序过程，一种主要的统计工具是统计过程控制 (SPC) 图，在过去的几十年里，它主要用于监控制造业的生产线。在最近的研究中开发了许多新的和通用的 SPC 方法，我们相信 SPC 可以成为处理许多超出生产线监控的大数据应用程序的强大工具。在本文中，我们将介绍一些最近的 SPC 方法，并讨论它们解决一些大数据问题的潜力。还讨论了当前 SPC 研究与一些大数据应用程序之间接口的某些挑战。