Kevin Bruhwiler - Academia.edu (original) (raw)

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Papers by Kevin Bruhwiler

Neutron scattering makes invaluable contributions to the physical, chemical, and nanostructured m... more Neutron scattering makes invaluable contributions to the physical, chemical, and nanostructured materials sciences. Single crystal diffraction experiments collect volumetric scattering data sets representing the internal structure relations by combining datasets of many individual settings at different orientations, times and sample environment conditions. In particular, we consider data from the single-crystal diffraction experiments at ORNL.* A new technical approach for rapid, interactive visualization of remote neutron data is being explored. The NVIDIA IndeX 3D volumetric visualization framework** is being used via the HTML5 client viewer from NVIDIA, the ParaView plugin***, and new Jupyter notebooks, which will be released to the community with an open source license.

Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT '19

2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2020

The growth in observational data volumes over the past decade has occurred alongside a need to ma... more The growth in observational data volumes over the past decade has occurred alongside a need to make sense of the phenomena that underpin them. Visualization is a key component of the data wrangling process that precedes the analyses that informs these insights. The crux of this study is interactive visualizations of spatiotemporal phenomena from voluminous datasets. Spatiotemporal visualizations of voluminous datasets introduce challenges relating to interactivity, overlaying multiple datasets and dynamic feature selection, resource capacity constraints, and scaling. In this study we describe our methodology to address these challenges. We rely on a novel mix of algorithms and systems innovations working in concert to ensure effective apportioning and amortization of workloads and enable interactivity during visualizations. In particular our research prototype, Iris, leverages sketching algorithms, effective query predicate generation and evaluation, avoids performance hotspots, har...

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)

2020 IEEE International Conference on Big Data (Big Data), 2020

There has been a substantial growth in remotely sensed hyperspectral satellite imagery. These dat... more There has been a substantial growth in remotely sensed hyperspectral satellite imagery. These data offer opportunities to understand phenomena and inform decision making. The nature of these collections introduces challenges stemming from their volumes, variety, and spatiotemporal resolutions. The crux of this study is to facilitate effective training of deep learning models over satellite data collections. We describe our novel embeddings (multidimensional latent space representations) based approach to effectively support model training, refinement, and inferences. We rigorously explore several aspects relating to embeddings, including their dimensionality, single vs multiple bands, and preservation of inter-band metrics. We also incorporate support for transfer learning over spatiotemporal scopes to address issues relating to cold start and alleviate resource pressure. Our methodology addresses disk, network, CPU/GPU, and accuracy implications of several aspects relating to model...

One of the most powerful ways to explore data is to visualize it. Visualizations underpin data wr... more One of the most powerful ways to explore data is to visualize it. Visualizations underpin data wrangling, feature space explorations, and understanding the dynamics of phenomena. Here, we explore interactive visualizations of voluminous, spatiotemporal datasets. Our system, Aperture, makes novel use of data sketches to reconcile I/O overheads, in particular the speed differential across the memory hierarchy, and data volumes. Queries underpin several aspects of our methodology. This includes support for a diversity of queries that are aligned with the construction of visual artifacts, facilitating their effective evaluation over the server (distributed) backend, and generating speculative queries based on a user's exploration trajectory. Aperture includes support for different visual artifacts, animations, and multilinked views via scalable brushing-and-linking. Finally, we also explore issues in effective containerization to support visualization workloads. Our empirical benchm...

Neutron scattering makes invaluable contributions to the physical, chemical, and nanostructured m... more Neutron scattering makes invaluable contributions to the physical, chemical, and nanostructured materials sciences. Single crystal diffraction experiments collect volumetric scattering data sets representing the internal structure relations by combining datasets of many individual settings at different orientations, times and sample environment conditions. In particular, we consider data from the single-crystal diffraction experiments at ORNL.* A new technical approach for rapid, interactive visualization of remote neutron data is being explored. The NVIDIA IndeX 3D volumetric visualization framework** is being used via the HTML5 client viewer from NVIDIA, the ParaView plugin***, and new Jupyter notebooks, which will be released to the community with an open source license.

Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT '19

2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2020

The growth in observational data volumes over the past decade has occurred alongside a need to ma... more The growth in observational data volumes over the past decade has occurred alongside a need to make sense of the phenomena that underpin them. Visualization is a key component of the data wrangling process that precedes the analyses that informs these insights. The crux of this study is interactive visualizations of spatiotemporal phenomena from voluminous datasets. Spatiotemporal visualizations of voluminous datasets introduce challenges relating to interactivity, overlaying multiple datasets and dynamic feature selection, resource capacity constraints, and scaling. In this study we describe our methodology to address these challenges. We rely on a novel mix of algorithms and systems innovations working in concert to ensure effective apportioning and amortization of workloads and enable interactivity during visualizations. In particular our research prototype, Iris, leverages sketching algorithms, effective query predicate generation and evaluation, avoids performance hotspots, har...

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)

2020 IEEE International Conference on Big Data (Big Data), 2020

There has been a substantial growth in remotely sensed hyperspectral satellite imagery. These dat... more There has been a substantial growth in remotely sensed hyperspectral satellite imagery. These data offer opportunities to understand phenomena and inform decision making. The nature of these collections introduces challenges stemming from their volumes, variety, and spatiotemporal resolutions. The crux of this study is to facilitate effective training of deep learning models over satellite data collections. We describe our novel embeddings (multidimensional latent space representations) based approach to effectively support model training, refinement, and inferences. We rigorously explore several aspects relating to embeddings, including their dimensionality, single vs multiple bands, and preservation of inter-band metrics. We also incorporate support for transfer learning over spatiotemporal scopes to address issues relating to cold start and alleviate resource pressure. Our methodology addresses disk, network, CPU/GPU, and accuracy implications of several aspects relating to model...

One of the most powerful ways to explore data is to visualize it. Visualizations underpin data wr... more One of the most powerful ways to explore data is to visualize it. Visualizations underpin data wrangling, feature space explorations, and understanding the dynamics of phenomena. Here, we explore interactive visualizations of voluminous, spatiotemporal datasets. Our system, Aperture, makes novel use of data sketches to reconcile I/O overheads, in particular the speed differential across the memory hierarchy, and data volumes. Queries underpin several aspects of our methodology. This includes support for a diversity of queries that are aligned with the construction of visual artifacts, facilitating their effective evaluation over the server (distributed) backend, and generating speculative queries based on a user's exploration trajectory. Aperture includes support for different visual artifacts, animations, and multilinked views via scalable brushing-and-linking. Finally, we also explore issues in effective containerization to support visualization workloads. Our empirical benchm...