Papers

Paradigm Papers

Authors: Alex Endert, Chao Han, Dipayan Maiti, Leanna House, Scotland Leman, Chris North

Description: The authors define the concept of an observation-level interaction (OLI), which allows for adjustments to spatial projections, in order to provide deep insights on the global relationships between the objects themselves. Specifically, the authors develop and implement two types of OLIs, namely exploratory and expressive interactions, whereby users are able to explore spatial patterns within the visualization, or provide adjustments based on expressed relationships between variables. Examples are provided for three statistical models: Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM).

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Authors: Scotland Leman, Leanna House, Dipayan Maiti, Alex Endert, Chris North

Description: The authors develop a bi-directional pipeline between both highly parameterized statistical models and their resulting visual output. This pipeline implements a general version of OLI, that encodes a user’s cognitive feedback into it’s parametric counterpart.  

Given a user’s cognitive insights between objects, V2PI provides an updated projection of the data, which is based on an optimal re-parameterization of the underlying statistical model and the user’s adjustments.

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Authors: Leanna House, Scotland Leman, Chao Han

Description: The authors develop a probabilistic version of V2PI called Bayesian Visual Analytics (BaVA). Examples are provided using probabilistic forms of principal component analysis/factor modeling and weighted multidimensional scaling using both simulated and real-datasets.  

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Methodology & Extensions

Authors: Chao Han, Leanna House, Scotland Leman

Description: The authors extend V2PI to non-linear projections using the Generative Topographical Modeling (GTM) framework. Examples are provided based on a high dimensional textual dataset.

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Authors: Xiran Hu, Lauren Bradel, Dipayan Maiti, Leanna House, Chris North, Scotland Leman

Description: The authors extend the V2PI framework to account for relationships in unmoved objects. This article introduces a novel weighting scheme that incorporates the significance of these unmoved points into V2PI.

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Authors: Lauren Bradel, Chris North, Leanna House, Scotland Leman

Description: This article introduces Multi-Model Semantic Interaction coupled with the generalized pipeline to address scalability issues of prior methods by operating on a multitude of data scales (e.g. local, database, and cloud scales).

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Authors: Alex Endert, Chao Han, Dipayan Maiti, Leanna House, Scotland Leman, Chris North

Description: The authors define the concept of an observation-level interaction (OLI), which allows for adjustments to spatial projections, in order to provide deep insights on the global relationships between the objects themselves. Specifically, the authors develop and implement two types of OLIs, namely exploratory and expressive interactions, whereby users are able to explore spatial patterns within the visualization, or provide adjustments based on expressed relationships between variables. Examples are provided for three statistical models: Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM).

View the full-text article in PDF here.

Authors: Leanna House, Scotland Leman, Chao Han

Description: The authors develop a probabilistic version of V2PI called Bayesian Visual Analytics (BaVA). Examples are provided using probabilistic forms of principal component analysis/factor modeling and weighted multidimensional scaling using both simulated and real-datasets.  

View the full-text article in PDF here.

User Studies & Pedagogy

Authors: Scotland Leman and Leanna House

Description: The authors propose an interactive data visualization (IDV) curriculum, which extends traditional teaching styles of data analytics using a balanced qualitative/quantitative approach.  This approach is aimed at enhancing students’ critical thinking and quantitative skills, while working with real-world, data-driven problems.

Authors: Scotland Leman, Leanna House, Andrew Hoegh

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Authors: Xin Chen, Jessica Zeitz Self, Leanna House, Chris North

Description: The article introduces an immersive data exploration system, Be the Data, that allows students to develop their critical thinking skills and high-dimensional cognition, within an informal learning environment.

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Authors: Jessica Zeitz Self, Radha Krishnan Vinayagam, J.T. Fry, Chris North

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Authors: Xin Chen, Jessica Zeitz Self, Maoyuan Sun, Leanna House, Chris North 

Summary:

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Authors: Lauren Bradel, Jessica Zeitz Self, Alex Endert, M. Shahriar Hossain, Chris North, Naren Ramakrishnan

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Authors: Jessica Zeitz Self, Nathan Self, Leanna House, Scotland Leman, Chris North

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Systems & Implementations

Authors: Jessica Zeitz Self, Leanna House, Scotland Leman, Chris North

Description: The authors introduce Andromeda, an interactive visual analytics tool that allows users to execute parametric-level and observational-level interactions in order to foster data exploration. Additionally, the authors discuss the uses and benefits of Andromeda along with the results of a controlled usability study.

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Authors: Jessica Zeitz Self, Xinran Hu, Leanna House, Scotland Leman, Chris North

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Authors: Lauren Bradel, Nathan Wycoff, Leanna House, Chris North

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Documentations & Technical Reports

Authors: Jessica Zeitz Self, Xinran Hu, Leanna House, Scotland Leman, Chris North

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Authors: Scotland Leman, Leanna House, Dipayan Maiti, Alex Endert, Chris North

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Authors: Jessica Zeitz Self, Nathan Self, Leanna House, Jane Robertson Evia, Scotland Leman, Chris North

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Authors: Leanna House, Scotland Leman, Chao Han

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