Recent advances in data acquisition technologies have led to massive amounts of data being collected routinely in the physical, chemical, and engineering sciences as well as information sciences and technology. In addition to volume, the data often have complicated structures.

Examples of such Big Data include the data streams obtained from complex engineering systems, image sequences, climate data, website transaction logs, and credit card records. Because of their big volume and complicated structure, Big Data are difficult to handle using traditional database management and statistical analysis tools. For instance, Big Data sets cannot be practically analyzed on a single computer because their sizes are often too large to fit in memory, or it is too time consuming to process using the current statistical methods. To circumvent this obstacle, one may have to resort to parallel and distributed architectures with multicore and cloud computing platforms that have access to hundreds or even thousands of processors.

While the parallel and distributed architectures present new capabilities for storage and manipulation of Big Data from an inferential point of view, it is unclear how the current statistical methodology can be transported to the paradigm of Big Data. Also, with growing data volume and complexity of data structures, the corresponding statistical models for properly describing the Big Data might need to be more sophisticated. Furthermore, with larger data comes the expectation of understanding the related scientific phenomena at a much deeper level than it would be possible with a moderately sized sample. For all these reasons, the advent of Big Data creates new challenges for current statistical methodology.

This special issue will publish original high-quality papers that deal with all aspects of the statistical analysis of Big Data, including but not limited to the following:

• Data visualization and exploratory data analysis
• Statistical computation
• Statistical modeling and inferences
• Innovative applications

Papers in any application domain that fit within the broad scope of Technometrics will be considered. Papers must be prepared in accordance with the Technometrics standards and guidelines. Submitted papers should be original, not previously published, and not under consideration for publication elsewhere. All papers will be reviewed following the regular review procedure of Technometrics.

Paper Submission

Submit your manuscript through Manuscript Central. Please select “Special Issue on Big Data” under Manuscript Type. If you have difficulties, please contact Editorial Coordinator Janet Wallace at technometrics.office@gmail.com.