PNNL

Abstract

Real-time systems often consist of high-volume, high-velocity data streams in which making efficient and timely inferences about the data are challenging. In particular, when a user interacts with a real-time system to gain insights, detect events, and make decisions about the system, the rate and amount of information the user is required to process is generally overwhelming. In addition, analytically processing large volumes of data can be computationally expensive and, in real-time, renders traditional inferential methods effectively useless. One approach to mitigate these challenges is to reduce both the amount of information presented to the user and the volume of data placed in the stream. In this manuscript we will describe a method constructed under this approach in addition to the development and deployment of an online tool, Real time Event Detector for Subsampled Images (REDSI), designed to provide feedback on a real-time system by characterizing and detecting events of interest. We will discuss REDSI in the context of scanning transmission electron microscopy (STEM), which is a powerful real-time system that provides high spatial and temporal resolution on nanoscale structures and processes. The data produced by STEM experiments are a stream of images relaying structural, compositional, and dynamic interphase information to scientists in fields ranging from microbiology and neuroscience to materials science and energetics. While REDSI was designed to processes STEM images in real time, we will also discuss other real-time system applications for this methodology.