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Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. In this paper, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system that allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. Although the focus of this paper is on additive manufacturing, the techniques described are applicable to the analysis of any quantitative time series.

Ultrascale Visualization of Climate DataUltrascale Visualization Climate Data Analysis Tools Project Team Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways. Fueled by exponential increases in the computational and storage capabilities of high-performance computing platforms, climate simulations are evolving toward higher numerical fidelity, complexity, volume, and dimensionality. These technological breakthroughs are coming at a time of exponential growth in climate data, with estimates of hundreds of exabytes by 2020. 1 To meet the challenges and exploit the opportunities that such explosive growth affords, a consortium of four national laboratories, two universities, a government agency, and two private companies formed to explore the next wave in climate science. Working in close collaboration with domain experts, the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) project aims to provide high-level solutions to a variety of climate data analysis and visualization problems:Dealing with big data analytics. Climate science is no different from other domains in its pursuit of solutions to process, analyze, and visualize massive datasets. Sensitivity analysis. The community must be able to push ensemble analysis, uncertainty quantification, and metrics computation to new boundaries. Heterogeneous data sources. Climate science data comes from simulations, observations, and reanalysis. Any visualization and analysis solution must unify these sources. Reproducibility. All science must support systematic data maintenance by providing provenance to ensure reliable and persistent links between workflows. Multiple disciplinary domains. Complexity stems from the need to incorporate a broad nexus of climate and other related science domains such as climate adaptation and mitigation for water, energy, and agriculture conservation. • Flexible, scalable architecture. Any unifying structure must be able to incorporate both existing and future software components with minimal or no infrastructure modification.

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