Editor's notes on Special Issue on “Statistical Process Control for Big Data Streams”

摘要

Recent advances in data acquisition technologies have led to massive amounts of data streams being collected routinely in the physical, chemical, and engineering sciences as well as information sciences and technology. A lot of information coming from structured and unstructured data streams (webpages, emails, text documents, images, videos, sensor data, etc.) needs to be aligned, integrated, analyzed and monitored to give companies and organizations new insights about their products, customers and services. Because of their big volume and complicated structure, big data streams are difficult to handle using traditional methods of statistical process control (SPC)/statistical process monitoring (SPM). New techniques for quickly detecting and diagnosing abnormal activities in sequences of massive amounts of complex data become increasingly crucial and important. The main purpose of this special issue is to promote innovative approaches for handling big data that arise in the area of SPC/SPM. It features seven original articles, which cover a wide range of topics from various aspects of the statistical process control for big data streams.