Abstract

[This abstract is based on the authors' abstract.]The increasing availability of high-volume, high-velocity data sets, often containing variables of dfferentdata types, brings an increasing need for monitoring tools that are designed to handle these big data sets.While the research on multivariate statistical process monitoring tools is vast, the application of thesetools for big data sets has received less attention. In this expository paper, we give an overview of thecurrent state of data-driven multivariate statistical process monitoring methodology. We highlight someof the main directions involving statistical learning and dimension reduction techniques applied to controlcharts in research from supply chain, engineering, computer science, and statistics. The goal of this paperis to bring into better focus some of the monitoring and surveillance methodology informed by data miningtechniques that show promise for monitoring large and diverse data sets. We introduce an example usingWikipedia search information and illustrate a few of the complexities of applying the available methods toa high-dimensional monitoring scenario. Throughout, we o↵er advice to practitioners and some suggestionsfor future research in this emerging area of research.