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Analytics Environment

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David S Ebert – One of the best experts on this subject based on the ideXlab platform.

  • a visual Analytics based decision making Environment for covid 19 modeling and visualization
    arXiv: Human-Computer Interaction, 2020
    Co-Authors: Shehzad Afzal, David S Ebert, Sohaib Ghani, Hank C Jenkinssmith, Markus Hadwiger, Ibrahim Hoteit
    Abstract:

    Public health officials dealing with pandemics like COVID-19 have to evaluate and prepare response plans. This planning phase requires not only looking into the spatiotemporal dynamics and impact of the pandemic using simulation models, but they also need to plan and ensure the availability of resources under different spread scenarios. To this end, we have developed a visual Analytics Environment that enables public health officials to model, simulate, and explore the spread of COVID-19 by supplying county-level information such as population, demographics, and hospital beds. This Environment facilitates users to explore spatiotemporal model simulation data relevant to COVID-19 through a geospatial map with linked statistical views, apply different decision measures at different points in time, and understand their potential impact. Users can drill-down to county-level details such as the number of sicknesses, deaths, needs for hospitalization, and variations in these statistics over time. We demonstrate the usefulness of this Environment through a use case study and also provide feedback from domain experts. We also provide details about future extensions and potential applications of this work.

  • a correlative analysis process in a visual Analytics Environment
    Visual Analytics Science and Technology, 2012
    Co-Authors: A Malik, Ross Maciejewski, Niklas Elmqvist, Yun Jang, David S Ebert, Whitney K Huang
    Abstract:

    Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual Analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson’s product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.

  • IEEE VAST – A correlative analysis process in a visual Analytics Environment
    2012 IEEE Conference on Visual Analytics Science and Technology (VAST), 2012
    Co-Authors: Abish Malik, Ross Maciejewski, Niklas Elmqvist, Yun Jang, David S Ebert, Whitney K Huang
    Abstract:

    Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual Analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson’s product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.

Ross Maciejewski – One of the best experts on this subject based on the ideXlab platform.

  • a correlative analysis process in a visual Analytics Environment
    Visual Analytics Science and Technology, 2012
    Co-Authors: A Malik, Ross Maciejewski, Niklas Elmqvist, Yun Jang, David S Ebert, Whitney K Huang
    Abstract:

    Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual Analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson’s product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.

  • IEEE VAST – A correlative analysis process in a visual Analytics Environment
    2012 IEEE Conference on Visual Analytics Science and Technology (VAST), 2012
    Co-Authors: Abish Malik, Ross Maciejewski, Niklas Elmqvist, Yun Jang, David S Ebert, Whitney K Huang
    Abstract:

    Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual Analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson’s product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.

  • describing temporal correlation spatially in a visual Analytics Environment
    Hawaii International Conference on System Sciences, 2011
    Co-Authors: A Malik, Ross Maciejewski, Erin M Hodgess, David S Ebert
    Abstract:

    In generating and exploring hypotheses, analysts often want to know about the relationship between data values across time and space. Often, the analysis begins at a world level view in which the overall temporal trend of the data is analyzed and linear correlations between various factors are explored. However, such an analysis often fails to take into account the underlying spatial structure within the data. In this work, we present an interactive visual Analytics system for exploring temporal linear correlations across a variety of spatial aggregations. Users can interactively select temporal regions of interest within a calendar view window. The correlation coefficient between the selected time series is automatically calculated and the resultant value is displayed to the user. Simultaneously, a linked geospatial viewing window of the data provides information on the temporal linear correlations of the selected spatial aggregation level. Linear correlation values between time series are displayed as a choropleth map using a divergent color scheme. Furthermore, the statistical significance of each linear correlation value is calculated and regions in which the correlation value falls within the 95% confidence interval are highlighted. In this manner, analysts are able to explore both the global temporal linear correlations, as well as the underlying spatial factors that may be influencing the overall trend.

Erin M Hodgess – One of the best experts on this subject based on the ideXlab platform.

  • describing temporal correlation spatially in a visual Analytics Environment
    Hawaii International Conference on System Sciences, 2011
    Co-Authors: A Malik, Ross Maciejewski, Erin M Hodgess, David S Ebert
    Abstract:

    In generating and exploring hypotheses, analysts often want to know about the relationship between data values across time and space. Often, the analysis begins at a world level view in which the overall temporal trend of the data is analyzed and linear correlations between various factors are explored. However, such an analysis often fails to take into account the underlying spatial structure within the data. In this work, we present an interactive visual Analytics system for exploring temporal linear correlations across a variety of spatial aggregations. Users can interactively select temporal regions of interest within a calendar view window. The correlation coefficient between the selected time series is automatically calculated and the resultant value is displayed to the user. Simultaneously, a linked geospatial viewing window of the data provides information on the temporal linear correlations of the selected spatial aggregation level. Linear correlation values between time series are displayed as a choropleth map using a divergent color scheme. Furthermore, the statistical significance of each linear correlation value is calculated and regions in which the correlation value falls within the 95% confidence interval are highlighted. In this manner, analysts are able to explore both the global temporal linear correlations, as well as the underlying spatial factors that may be influencing the overall trend.

  • HICSS – Describing Temporal Correlation Spatially in a Visual Analytics Environment
    2011 44th Hawaii International Conference on System Sciences, 2011
    Co-Authors: Abish Malik, Ross Maciejewski, Erin M Hodgess, David S Ebert
    Abstract:

    In generating and exploring hypotheses, analysts often want to know about the relationship between data values across time and space. Often, the analysis begins at a world level view in which the overall temporal trend of the data is analyzed and linear correlations between various factors are explored. However, such an analysis often fails to take into account the underlying spatial structure within the data. In this work, we present an interactive visual Analytics system for exploring temporal linear correlations across a variety of spatial aggregations. Users can interactively select temporal regions of interest within a calendar view window. The correlation coefficient between the selected time series is automatically calculated and the resultant value is displayed to the user. Simultaneously, a linked geospatial viewing window of the data provides information on the temporal linear correlations of the selected spatial aggregation level. Linear correlation values between time series are displayed as a choropleth map using a divergent color scheme. Furthermore, the statistical significance of each linear correlation value is calculated and regions in which the correlation value falls within the 95% confidence interval are highlighted. In this manner, analysts are able to explore both the global temporal linear correlations, as well as the underlying spatial factors that may be influencing the overall trend.

A Malik – One of the best experts on this subject based on the ideXlab platform.

  • a correlative analysis process in a visual Analytics Environment
    Visual Analytics Science and Technology, 2012
    Co-Authors: A Malik, Ross Maciejewski, Niklas Elmqvist, Yun Jang, David S Ebert, Whitney K Huang
    Abstract:

    Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual Analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson’s product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.

  • describing temporal correlation spatially in a visual Analytics Environment
    Hawaii International Conference on System Sciences, 2011
    Co-Authors: A Malik, Ross Maciejewski, Erin M Hodgess, David S Ebert
    Abstract:

    In generating and exploring hypotheses, analysts often want to know about the relationship between data values across time and space. Often, the analysis begins at a world level view in which the overall temporal trend of the data is analyzed and linear correlations between various factors are explored. However, such an analysis often fails to take into account the underlying spatial structure within the data. In this work, we present an interactive visual Analytics system for exploring temporal linear correlations across a variety of spatial aggregations. Users can interactively select temporal regions of interest within a calendar view window. The correlation coefficient between the selected time series is automatically calculated and the resultant value is displayed to the user. Simultaneously, a linked geospatial viewing window of the data provides information on the temporal linear correlations of the selected spatial aggregation level. Linear correlation values between time series are displayed as a choropleth map using a divergent color scheme. Furthermore, the statistical significance of each linear correlation value is calculated and regions in which the correlation value falls within the 95% confidence interval are highlighted. In this manner, analysts are able to explore both the global temporal linear correlations, as well as the underlying spatial factors that may be influencing the overall trend.

Christopher J. Harris – One of the best experts on this subject based on the ideXlab platform.

  • Open chemistry: RESTful web APIs, JSON, NWChem and the modern web application
    Journal of Cheminformatics, 2017
    Co-Authors: Marcus D. Hanwell, Wibe A. Jong, Christopher J. Harris
    Abstract:

    An end-to-end platform for chemical science research has been developed that integrates data from computational and experimental approaches through a modern web-based interface. The platform offers an interactive visualization and Analytics Environment that functions well on mobile, laptop and desktop devices. It offers pragmatic solutions to ensure that large and complex data sets are more accessible. Existing desktop applications/frameworks were extended to integrate with high-performance computing resources, and offer command-line tools to automate interaction—connecting distributed teams to this software platform on their own terms. The platform was developed openly, and all source code hosted on the GitHub platform with automated deployment possible using Ansible coupled with standard Ubuntu-based machine images deployed to cloud machines. The platform is designed to enable teams to reap the benefits of the connected web—going beyond what conventional search and Analytics platforms offer in this area. It also has the goal of offering federated instances, that can be customized to the sites/research performed. Data gets stored using JSON, extending upon previous approaches using XML, building structures that support computational chemistry calculations. These structures were developed to make it easy to process data across different languages, and send data to a JavaScript-based web client.