The Experts below are selected from a list of 6306 Experts worldwide ranked by ideXlab platform

Jason H. Moore - One of the best experts on this subject based on the ideXlab platform.

  • treeheatr: an R Package for interpretable decision tree visualizations.
    Bioinformatics (Oxford England), 2020
    Co-Authors: Jason H. Moore
    Abstract:

    Summary treeheatr is an R Package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily Installed Package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students' understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. Availability The treeheatr Package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/Package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous integration. Supplementary information Supplementary data are available at Bioinformatics online.

  • treeheatr: an R Package for interpretable decision tree visualizations
    2020
    Co-Authors: Jason H. Moore
    Abstract:

    treeheatr is an R Package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree9s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily Installed Package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students9 understanding of a simple decision tree model before diving into more complex tree-based machine learning methods.

Satoshi Matsuoka - One of the best experts on this subject based on the ideXlab platform.

  • model based resource selection for efficient virtual cluster deployment
    Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing (VTDC '07), 2007
    Co-Authors: Shohei Yamasaki, Naoya Maruyama, Satoshi Matsuoka
    Abstract:

    Virtual clusters on Grids can greatly extend the current scale and efficiency of high-performance Grid computing through more flexible usage of distributed heterogeneous resources. However, the overhead due to installing virtual clusters often precludes their applicability only to long-running applications. This is especially problematic on heterogeneous Grid environments, since installation time of each virtual node can vary greatly, and the total installation time of a virtual cluster is bottlenecked by the slowest node. To achieve fast virtual cluster installation even on such heterogeneous Grid environments, we propose a model-based resource selection policy that automatically identifies a near-optimal node combination to assemble each virtual cluster. We divide the VM setup process into five logical steps and construct a performance model for each step. The model represents the execution time of each step as a linear combination of hardware and software parameters, including CPU frequency, disk I/O performance, and Installed Package size. To evaluate the proposed resource selection policy, we have extended our own virtual cluster installer, VPC, to select nodes in the increasing order of predicted installation time. Experimental results show that the model-based selection policy is indeed effective, especially when the Package size differs depending on sites. The proposed policy has shown to reduce the installation time by up to 68% compared to the most naive policy that selects nodes in a random order, 60% and 58% to the policies considering either CPU speed or disk I/O performance, respectively.

  • VTDC@SC - Model-based resource selection for efficient virtual cluster deployment
    Proceedings of the 3rd international workshop on Virtualization technology in distributed computing - VTDC '07, 2007
    Co-Authors: Shohei Yamasaki, Naoya Maruyama, Satoshi Matsuoka
    Abstract:

    Virtual clusters on Grids can greatly extend the current scale and efficiency of high-performance Grid computing through more flexible usage of distributed heterogeneous resources. However, the overhead due to installing virtual clusters often precludes their applicability only to long-running applications. This is especially problematic on heterogeneous Grid environments, since installation time of each virtual node can vary greatly, and the total installation time of a virtual cluster is bottlenecked by the slowest node. To achieve fast virtual cluster installation even on such heterogeneous Grid environments, we propose a model-based resource selection policy that automatically identifies a near-optimal node combination to assemble each virtual cluster. We divide the VM setup process into five logical steps and construct a performance model for each step. The model represents the execution time of each step as a linear combination of hardware and software parameters, including CPU frequency, disk I/O performance, and Installed Package size. To evaluate the proposed resource selection policy, we have extended our own virtual cluster installer, VPC, to select nodes in the increasing order of predicted installation time. Experimental results show that the model-based selection policy is indeed effective, especially when the Package size differs depending on sites. The proposed policy has shown to reduce the installation time by up to 68% compared to the most naive policy that selects nodes in a random order, 60% and 58% to the policies considering either CPU speed or disk I/O performance, respectively.

Shohei Yamasaki - One of the best experts on this subject based on the ideXlab platform.

  • model based resource selection for efficient virtual cluster deployment
    Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing (VTDC '07), 2007
    Co-Authors: Shohei Yamasaki, Naoya Maruyama, Satoshi Matsuoka
    Abstract:

    Virtual clusters on Grids can greatly extend the current scale and efficiency of high-performance Grid computing through more flexible usage of distributed heterogeneous resources. However, the overhead due to installing virtual clusters often precludes their applicability only to long-running applications. This is especially problematic on heterogeneous Grid environments, since installation time of each virtual node can vary greatly, and the total installation time of a virtual cluster is bottlenecked by the slowest node. To achieve fast virtual cluster installation even on such heterogeneous Grid environments, we propose a model-based resource selection policy that automatically identifies a near-optimal node combination to assemble each virtual cluster. We divide the VM setup process into five logical steps and construct a performance model for each step. The model represents the execution time of each step as a linear combination of hardware and software parameters, including CPU frequency, disk I/O performance, and Installed Package size. To evaluate the proposed resource selection policy, we have extended our own virtual cluster installer, VPC, to select nodes in the increasing order of predicted installation time. Experimental results show that the model-based selection policy is indeed effective, especially when the Package size differs depending on sites. The proposed policy has shown to reduce the installation time by up to 68% compared to the most naive policy that selects nodes in a random order, 60% and 58% to the policies considering either CPU speed or disk I/O performance, respectively.

  • VTDC@SC - Model-based resource selection for efficient virtual cluster deployment
    Proceedings of the 3rd international workshop on Virtualization technology in distributed computing - VTDC '07, 2007
    Co-Authors: Shohei Yamasaki, Naoya Maruyama, Satoshi Matsuoka
    Abstract:

    Virtual clusters on Grids can greatly extend the current scale and efficiency of high-performance Grid computing through more flexible usage of distributed heterogeneous resources. However, the overhead due to installing virtual clusters often precludes their applicability only to long-running applications. This is especially problematic on heterogeneous Grid environments, since installation time of each virtual node can vary greatly, and the total installation time of a virtual cluster is bottlenecked by the slowest node. To achieve fast virtual cluster installation even on such heterogeneous Grid environments, we propose a model-based resource selection policy that automatically identifies a near-optimal node combination to assemble each virtual cluster. We divide the VM setup process into five logical steps and construct a performance model for each step. The model represents the execution time of each step as a linear combination of hardware and software parameters, including CPU frequency, disk I/O performance, and Installed Package size. To evaluate the proposed resource selection policy, we have extended our own virtual cluster installer, VPC, to select nodes in the increasing order of predicted installation time. Experimental results show that the model-based selection policy is indeed effective, especially when the Package size differs depending on sites. The proposed policy has shown to reduce the installation time by up to 68% compared to the most naive policy that selects nodes in a random order, 60% and 58% to the policies considering either CPU speed or disk I/O performance, respectively.

Naoya Maruyama - One of the best experts on this subject based on the ideXlab platform.

  • model based resource selection for efficient virtual cluster deployment
    Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing (VTDC '07), 2007
    Co-Authors: Shohei Yamasaki, Naoya Maruyama, Satoshi Matsuoka
    Abstract:

    Virtual clusters on Grids can greatly extend the current scale and efficiency of high-performance Grid computing through more flexible usage of distributed heterogeneous resources. However, the overhead due to installing virtual clusters often precludes their applicability only to long-running applications. This is especially problematic on heterogeneous Grid environments, since installation time of each virtual node can vary greatly, and the total installation time of a virtual cluster is bottlenecked by the slowest node. To achieve fast virtual cluster installation even on such heterogeneous Grid environments, we propose a model-based resource selection policy that automatically identifies a near-optimal node combination to assemble each virtual cluster. We divide the VM setup process into five logical steps and construct a performance model for each step. The model represents the execution time of each step as a linear combination of hardware and software parameters, including CPU frequency, disk I/O performance, and Installed Package size. To evaluate the proposed resource selection policy, we have extended our own virtual cluster installer, VPC, to select nodes in the increasing order of predicted installation time. Experimental results show that the model-based selection policy is indeed effective, especially when the Package size differs depending on sites. The proposed policy has shown to reduce the installation time by up to 68% compared to the most naive policy that selects nodes in a random order, 60% and 58% to the policies considering either CPU speed or disk I/O performance, respectively.

  • VTDC@SC - Model-based resource selection for efficient virtual cluster deployment
    Proceedings of the 3rd international workshop on Virtualization technology in distributed computing - VTDC '07, 2007
    Co-Authors: Shohei Yamasaki, Naoya Maruyama, Satoshi Matsuoka
    Abstract:

    Virtual clusters on Grids can greatly extend the current scale and efficiency of high-performance Grid computing through more flexible usage of distributed heterogeneous resources. However, the overhead due to installing virtual clusters often precludes their applicability only to long-running applications. This is especially problematic on heterogeneous Grid environments, since installation time of each virtual node can vary greatly, and the total installation time of a virtual cluster is bottlenecked by the slowest node. To achieve fast virtual cluster installation even on such heterogeneous Grid environments, we propose a model-based resource selection policy that automatically identifies a near-optimal node combination to assemble each virtual cluster. We divide the VM setup process into five logical steps and construct a performance model for each step. The model represents the execution time of each step as a linear combination of hardware and software parameters, including CPU frequency, disk I/O performance, and Installed Package size. To evaluate the proposed resource selection policy, we have extended our own virtual cluster installer, VPC, to select nodes in the increasing order of predicted installation time. Experimental results show that the model-based selection policy is indeed effective, especially when the Package size differs depending on sites. The proposed policy has shown to reduce the installation time by up to 68% compared to the most naive policy that selects nodes in a random order, 60% and 58% to the policies considering either CPU speed or disk I/O performance, respectively.

I Komarov - One of the best experts on this subject based on the ideXlab platform.

  • Main Features of the ScientificVR ® Visualization Package.
    2002
    Co-Authors: A Zibarov, D Babayev, A Mironov, I Komarov
    Abstract:

    This article is devoted to description of architecture and abilities of new visualization system ScientificVR � � � � , visualizing different data formats and handling the abstract type of information. Most outstanding features of the Package are plug-in architecture, semitransparent voxel graphic and full color 3D stereoscopic presentations. The ScientificVR � �� is based on modular architecture, which makes it possible to endow the already Installed Package with new capabilities by way of hooking up required external modules. Thus, the same Package could be used for handling diverse data in wide range of applications.