The Experts below are selected from a list of 157566 Experts worldwide ranked by ideXlab platform
David Gotz - One of the best experts on this subject based on the ideXlab platform.
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progressive visual analytics user driven visual exploration of in progress analytics
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Charles D. Stolper, Adam Perer, David GotzAbstract:Fig. 1. The Progressive Insights system features progressive visual analytics, and supports user-driven exploration of in-progress analytics. Partial results from the progressive analytics enhance the scatterplot, list, and tree visualizations without interfering with users' cognitive workflow. Abstract— As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-launching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The Paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics Paradigm; Design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics Paradigm by clinical researchers analyzing electronic medical records. Index Terms—Progressive visual analytics, information visualization, interactive machine learning, electronic medical records.
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Progressive visual analytics: User-driven visual exploration of in-progress analytics
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Charles D. Stolper, Adam Perer, David GotzAbstract:As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The Paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics Paradigm; Design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics Paradigm by clinical researchers analyzing electronic medical records.
Charles D. Stolper - One of the best experts on this subject based on the ideXlab platform.
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progressive visual analytics user driven visual exploration of in progress analytics
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Charles D. Stolper, Adam Perer, David GotzAbstract:Fig. 1. The Progressive Insights system features progressive visual analytics, and supports user-driven exploration of in-progress analytics. Partial results from the progressive analytics enhance the scatterplot, list, and tree visualizations without interfering with users' cognitive workflow. Abstract— As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-launching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The Paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics Paradigm; Design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics Paradigm by clinical researchers analyzing electronic medical records. Index Terms—Progressive visual analytics, information visualization, interactive machine learning, electronic medical records.
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Progressive visual analytics: User-driven visual exploration of in-progress analytics
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Charles D. Stolper, Adam Perer, David GotzAbstract:As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The Paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics Paradigm; Design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics Paradigm by clinical researchers analyzing electronic medical records.
Adam Perer - One of the best experts on this subject based on the ideXlab platform.
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progressive visual analytics user driven visual exploration of in progress analytics
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Charles D. Stolper, Adam Perer, David GotzAbstract:Fig. 1. The Progressive Insights system features progressive visual analytics, and supports user-driven exploration of in-progress analytics. Partial results from the progressive analytics enhance the scatterplot, list, and tree visualizations without interfering with users' cognitive workflow. Abstract— As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-launching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The Paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics Paradigm; Design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics Paradigm by clinical researchers analyzing electronic medical records. Index Terms—Progressive visual analytics, information visualization, interactive machine learning, electronic medical records.
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Progressive visual analytics: User-driven visual exploration of in-progress analytics
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Charles D. Stolper, Adam Perer, David GotzAbstract:As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The Paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics Paradigm; Design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics Paradigm by clinical researchers analyzing electronic medical records.
Michael C Stevens - One of the best experts on this subject based on the ideXlab platform.
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fmri task parameters influence hemodynamic activity in regions implicated in mental set switching
NeuroImage, 2013Co-Authors: Suzanne T Witt, Michael C StevensAbstract:Mental set switching is a complex executive function that is required when the focus of attention must be altered in order to adapt to a frequently-changing environment. While there is generally acceptance that switching is subserved by a fronto-parietal network, there is a considerable lack of consistency across studies as to other brain regions involved in executing mental set switches. This functional magnetic resonance imaging study sought to determine whether Paradigmatic Design aspects such as stimulus complexity, motor response complexity, and stimulus ordering could account for the differences in reporting of brain regions associated with mental set switching across previous studies. Several brain regions, including the striatum and anterior cingulate, previously associated with mental set switching were found to be related more to resolving intra-stimulus interference conferred by increased stimulus complexity and increased motor response complexity than to executing the mental set switch. In considering stimulus ordering, defined as the number of non-switch trials preceding a switch trial, brain activity was not observed in the fronto-parietal regions typically associated with switching but rather in regions in the anterior prefrontal cortex, sensorimotor cortex, and secondary visual cortices. Our results indicate that these important Paradigm Design aspects that are theoretically unrelated to set switching per se should be balanced and controlled for in future experiments, so as not to obscure clear identification of brain regions truly engaged in mental set switching.
Nicolae Nistor - One of the best experts on this subject based on the ideXlab platform.
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mass customization as an educational Paradigm Design and pilot evaluation of a mass customized problem based learning environment
International Conference on Advanced Learning Technologies, 2006Co-Authors: Nicolae NistorAbstract:Several authors point at mass customization as an application that is already proven and established in the economical sector. They demand similar applications in education, aimed at adapting courses and training to the individual characteristics of a high number of students, but without causing additional costs. However the available educational research literature provides neither explicit Design principles for mass-customized learning environments nor examples of such applications. This contribution formulates in its first section four Design principles of MC, deduced from a literature review in the field of economics. It shows related work from the educational field, discussing its relevance for MC as well as its limitations. The second part presents amittrade, an example of mass-customized, problem-based learning environment, along with the pilot evaluation results and some conclusions for further development and research
