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Analysis in Motion | Pacific Northwest National Laboratory

Pacific Northwest National Laboratory. Analysis In Motion Initiative. Interactive Streaming Analytics at Scale. In a world where data are continually streaming from distributed and diverse sources from scientific instruments, to web traffic, to live imagery making timely discoveries requires computing capabilities that can keep pace with rapidly evolving phenomena. Analysis in Motion Projects. Read more about Compressive Analysis. Mitigating Cognitive Depletion in Streaming Environments. Temporal Modelin...

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Analysis in Motion | Pacific Northwest National Laboratory | aim.pnnl.gov Reviews

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Pacific Northwest National Laboratory. Analysis In Motion Initiative. Interactive Streaming Analytics at Scale. In a world where data are continually streaming from distributed and diverse sources from scientific instruments, to web traffic, to live imagery making timely discoveries requires computing capabilities that can keep pace with rapidly evolving phenomena. Analysis in Motion Projects. Read more about Compressive Analysis. Mitigating Cognitive Depletion in Streaming Environments. Temporal Modelin...

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Analysis in Motion | Pacific Northwest National Laboratory | aim.pnnl.gov Reviews

https://aim.pnnl.gov

Pacific Northwest National Laboratory. Analysis In Motion Initiative. Interactive Streaming Analytics at Scale. In a world where data are continually streaming from distributed and diverse sources from scientific instruments, to web traffic, to live imagery making timely discoveries requires computing capabilities that can keep pace with rapidly evolving phenomena. Analysis in Motion Projects. Read more about Compressive Analysis. Mitigating Cognitive Depletion in Streaming Environments. Temporal Modelin...

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Streaming Data Characterization | Analysis in Motion

https://aim.pnnl.gov/projects/streaming-data-characterization

Pacific Northwest National Laboratory. Read more about Dr. Mark Greaves. SDC algorithms will directly benefit:. Stream processing applications where human operators have to use their expertise to help define the important facts to remember, such as cyber detection systems. Any stream processing system where the window size cannot be determined in advance. Pacific Northwest National Laboratory.

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Temporal Modeling for Streaming Analytics | Analysis in Motion

https://aim.pnnl.gov/projects/temporal-modeling-streaming-analytics

Pacific Northwest National Laboratory. Temporal Modeling for Streaming Analytics. Dr Lisa M. Bramer. Dr Lisa M. Bramer. Read more about Dr. Lisa M. Bramer. This research data will be useful for anyone who wants to make data-driven decisions. These methods will be general enough that they should be applicable over most domain spaces. These methods will outperform traditional statistical models and algorithms that ignore temporal structure that are currently being used on streaming data.

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Publications | Analysis in Motion

https://aim.pnnl.gov/publications

Pacific Northwest National Laboratory. Explanatory Visual Analytics for Enhancing Human Interpretability of Machine Learning Models. SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces. Too Much Time, Not Enough Space: Multi-Scale Event Sequence Visual Analytics with m-SVEN. Visual analytic systems have long relied on user studies and standard datasets to demonstrate advances to the state of the art, as well as to illustrate the efficiency of solutions ...

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Towards Enabling Complex Sensemaking from Streaming Data | Analysis in Motion

https://aim.pnnl.gov/projects/towards-enabling-complex-sensemaking-streaming-data

Pacific Northwest National Laboratory. Towards Enabling Complex Sensemaking from Streaming Data. Read more about Lyndsey Franklin. Sensemaking is a cognitively demanding process and has been the focus of considerable research from varied disciplines including cognitive psychology, neuroscience, and information sciences. We bring this research into streaming environments now to learn how to support tasks beyond simple anomaly detection and situation awareness. How much information they can handle at once.

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Transpire: Transparent Model-Driven Discovery of Streaming Patterns | Analysis in Motion

https://aim.pnnl.gov/projects/transpire-transparent-model-driven-discovery-streaming-patterns

Pacific Northwest National Laboratory. Transpire: Transparent Model-Driven Discovery of Streaming Patterns. Read more about Dr. Aritra Dasgupta. Most existing visual analytics techniques are based on assumptions about how human-stream feedback should work and are not supported by concrete empirical evidence. In our work we fill these gaps through three main contributions:. Study how differences in model transparency affect efficiency, effectiveness and the trustworthiness of human insights.

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PNNL: National Security Directorate

http://www.pnnl.gov/nationalsecurity

Pacific Northwest National Laboratory. Skip to Main Content. US Department of Energy. Our researchers within National Security at Pacific Northwest National Laboratory. Featuring NNSA Graduate Fellowship Program. The latest issue of the DNN Sentinel newsletter. Contains interviews of the NNSA Graduate Fellowship Program (NGFP) Class of 2015-2016 Fellows supporting the Office of Defense Nuclear Nonproliferation. The article introduces this year's 15 DNN Fellows, including what attracted them to the fi...

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Analysis in Motion | Pacific Northwest National Laboratory

Pacific Northwest National Laboratory. Analysis In Motion Initiative. Interactive Streaming Analytics at Scale. In a world where data are continually streaming from distributed and diverse sources from scientific instruments, to web traffic, to live imagery making timely discoveries requires computing capabilities that can keep pace with rapidly evolving phenomena. Analysis in Motion Projects. Read more about Compressive Analysis. Mitigating Cognitive Depletion in Streaming Environments. Temporal Modelin...

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