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Towards Adaptive, Streaming Analysis of X-ray Tomography Data

Temporal and spatial resolution of chemical imaging methodologies such as X-ray tomography are rapidly increasing, leading to more complex experimental procedures and fast-growing data volumes. Automated analysis pipelines and big data analytics are becoming essential to effectively evaluate the results of such experiments. Offering those data techniques in an adaptive, streaming environment can further substantially improve the scientific discovery process by enabling experimental control and steering based on the evaluation of emerging phenomena as they are observed by the experiment. Pacific Northwest National Laboratory (PNNL)'s Chemical Imaging Initiative (CII, http://imaging.pnnl.gov/) has worked since 2011 towards developing a framework that allows users to rapidly compose and customize high-throughput experimental analysis pipelines for multiple instrument types. The framework, named “Rapid Experimental Analysis” (REXAN) Framework [1], is based on the idea of reusable component libraries and utilizes the PNNL-developed collaborative data management and analysis environment “Velo” to provide a user-friendly analysis and data management environment for experimental facilities. This article will discuss the capabilities established for X-ray tomography, review lessons learned, and provide an overview of our more recent work in the Analysis in Motion Initiative (AIM, http://aim.pnnl.gov/) at PNNL to provide REXAN capabilities in a streaming environment.