TY - JOUR
T1 - Interactive Visual Analytics for Sensemaking with Big Text
JF - Big Data Research
Y1 - 2019
A1 - Michelle Dowling
A1 - Nathan Wycoff
A1 - Brian Mayer
A1 - Wenskovitch, John
A1 - Leman, Scotland
A1 - House, Leanna
A1 - Nicholas Polys
A1 - North, Chris
A1 - Peter Hauck
KW - Big data
KW - interactive visual analytics
KW - Semantic interaction
KW - text analytics
KW - Topic modeling
KW - visualization
AB - Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles.
VL - 16
UR - http://www.sciencedirect.com/science/article/pii/S2214579618302995
ER -
TY - Generic
T1 - Uncertainty in Interactive WMDS Visualizations
T2 - 2019 Symposium on Visualization in Data Science Posters
Y1 - 2019
A1 - Lata Kodali
A1 - Wenskovitch, John
A1 - Nathan Wycoff
A1 - House, Leanna
A1 - North, Chris
KW - poster
AB - Visualizations are useful when learning from high-dimensional data. However, visualizations can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. In our work, we extend a framework called Bayesian Visual Analytics (BaVA) on a dimension reduction algorithm, Weighted Multidimensional Scaling (WMDS), to incorporate uncertainty as analysts explore data visually. BaVA-WMDS visualizations are interactive, and possible interactions include manipulating variable weights and/or the coordinates of the two-dimensional projection. Uncertainty exists in these visualizations on the variable weights, the user interactions, and the fit of WMDS. We quantify these uncertainties using Bayesian models exploring randomness in both coordinates and weights in a method we call Interactive Probabilistic WMDS (IP-WMDS). Specifically, we use posterior estimates to assess fit of WMDS, the range of motion of coordinates, as well as variability in weights. Visually, we display such uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in fitting a dimension reduction algorithm. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts.
JF - 2019 Symposium on Visualization in Data Science Posters
T3 - VDS'19
CY - Vancouver, BC, Canada
ER -
TY - JOUR
T1 - Be the Data: Embodied Visual Analytics
JF - IEEE Transactions on Learning Technologies
Y1 - 2018
A1 - Xin Chen
A1 - Self, Jessica Zeitz
A1 - House, Leanna
A1 - Wenskovitch, John
A1 - Sun, Maoyuan
A1 - Nathan Wycoff
A1 - Jane Robertson Evia
A1 - Leman, Scotland
A1 - North, Chris
VL - 11
IS - 1
ER -
TY - CONF
T1 - The Effect of Semantic Interaction on Foraging in Text Analysis
T2 - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST)
Y1 - 2018
A1 - Wenskovitch, John
A1 - Lauren Bradel
A1 - Michelle Dowling
A1 - House, Leanna
A1 - North, Chris
AB - Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users' semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), "semantic interaction foraging" (SIF) occurs as a result of the user's synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.
JF - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST)
ER -
TY - JOUR
T1 - Observation-Level and Parametric Interaction for High-Dimensional Data Analysis
JF - ACM Transactions on Interactive Intelligent Systems
Y1 - 2018
A1 - Self, Jessica Zeitz
A1 - Michelle Dowling
A1 - Wenskovitch, John
A1 - Ian Crandell
A1 - Ming Wang
A1 - House, Leanna
A1 - Leman, Scotland
A1 - North, Chris
VL - 8
IS - 2
ER -
TY - CONF
T1 - SIRIUS: Dual, Symmetric, Interactive Dimension Reductions
T2 - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST)
Y1 - 2018
A1 - Michelle Dowling
A1 - Wenskovitch, John
A1 - J.T. Fry
A1 - Leman, Scotland
A1 - House, Leanna
A1 - North, Chris
KW - attribute projection
KW - dimension reduction
KW - exploratory data analysis
KW - observation projection
KW - Semantic interaction
AB - Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.
JF - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST)
ER -
TY - Generic
T1 - Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics
Y1 - 2018
A1 - Wenskovitch, John
A1 - Ian Crandell
A1 - Ramakrishnan, Naren
A1 - House, Leanna
A1 - Leman, Scotland
A1 - North, Chris
KW - Algorithm design and analysis
KW - clustering
KW - Clustering algorithms
KW - Data visualization
KW - Dimension reduction;algorithms
KW - Manifolds
KW - Partitioning algorithms
KW - Visual Analytics
KW - visualization
JF - IEEE Transactions on Visualization and Computer Graphics
VL - 24
ER -
TY - CONF
T1 - Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills
T2 - Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference
Y1 - 2017
A1 - Self, Jessica Zeitz
A1 - Self, Nathan
A1 - House, Leanna
A1 - Jane Robertson Evia
A1 - Leman, Scotland
A1 - North, Chris
JF - Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference
ER -
TY - CONF
T1 - Be the Data: A New Approach for Immersive Analytics
T2 - IEEE Virtual Reality 2016 Workshop on Immersive Analytics
Y1 - 2016
A1 - Xin Chen
A1 - Self, Jessica Zeitz
A1 - House, Leanna
A1 - North, Chris
JF - IEEE Virtual Reality 2016 Workshop on Immersive Analytics
ER -
TY - CONF
T1 - Be the Data: An Embodied Experience for Data Analytics
T2 - 2016 Annual Meeting of the American Educational Research Association (AERA)
Y1 - 2016
A1 - Xin Chen
A1 - House, Leanna
A1 - Self, Jessica Zeitz
A1 - Leman, Scotland
A1 - Jane Robertson Evia
A1 - James Thomas Fry
A1 - North, Chris
JF - 2016 Annual Meeting of the American Educational Research Association (AERA)
ER -
TY - CONF
T1 - Be the Data: Social Meetings with Visual Analytics
T2 - International Workshop on Visualization and Collaboration (VisualCol 2016)
Y1 - 2016
A1 - Xin Chen
A1 - Self, Jessica Zeitz
A1 - Sun, Maoyuan
A1 - House, Leanna
A1 - North, Chris
JF - International Workshop on Visualization and Collaboration (VisualCol 2016)
ER -
TY - CONF
T1 - Designing Usable Interactive Visual Analytics Tools for Dimension Reduction
T2 - CHI 2016 Workshop on Human-Centered Machine Learning (HCML)
Y1 - 2016
A1 - Self, Jessica Zeitz
A1 - Hu, Xinran
A1 - House, Leanna
A1 - Leman, Scotland
A1 - North, Chris
JF - CHI 2016 Workshop on Human-Centered Machine Learning (HCML)
ER -
TY - RPRT
T1 - Andromeda: Observation-Level and Parametric Interaction for Exploratory Data Analysis
Y1 - 2015
A1 - Self, Jessica Zeitz
A1 - House, Leanna
A1 - Leman, Scotland
A1 - North, Chris
AB - Exploring high-dimensional number of dimensions in datasets increases, it becomes harder to discover patterns and develop insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be tweaked to assess the data from multiple perspectives. Alas, tweaking can be difficult for users without a strong knowledge base of the underlying algorithms. We present Andromeda, an interactive visual analytics tool we developed to enable non-experts of statistical models to explore domain- specific, high-dimensional data. This application implements interactive weighted multidimensional scaling (WMDS) and allows for both parametric and observation- level interaction to provide in-depth data exploration. In this paper, we present the results of a controlled usability study assessing Andromeda. We focus on the comparison of parametric interaction, observation-level interaction and a combination of the two.
PB - Virginia Tech
CY - Blacksburg
ER -
TY - CONF
T1 - Big Text Visual Analytics in Sensemaking
T2 - IEEE International Symposium on Big Data Visual Analytics
Y1 - 2015
A1 - Lauren Bradel
A1 - Nathan Wycoff
A1 - House, Leanna
A1 - North, Chris
JF - IEEE International Symposium on Big Data Visual Analytics
ER -
TY - RPRT
T1 - Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills
Y1 - 2015
A1 - Self, Jessica Zeitz
A1 - Self, Nathan
A1 - House, Leanna
A1 - Jane Robertson Evia
A1 - Leman, Scotland
A1 - North, Chris
KW - dimension reduction
KW - education
KW - multidimensional scaling
KW - multivariate analysis
KW - Visual Analytics
AB - This paper addresses the use of visual analytics in education for teaching what we call cognitive dimensionality (CD) and other EDA skills. We present the concept of CD to characterize students' capacity for making dimensionally complex insights from data. Using this concept, we build a vocabulary and methodology to support a student’s progression in terms of growth from low cognitive dimensionality (LCD) to high cognitive dimensionality (HCD). Crucially, students do not need high-level math skills to develop HCD. Rather, we use our own tool called Andromeda that enables human-computer interaction with a common, easy to interpret visualization method called Weighted Multidimensional Scaling (WMDS) to promote the idea of making high-dimensional insights. In this paper, we present Andromeda and report findings from a series of classroom assignments to 18 graduate students. These assignments progress from spreadsheet manipulations to statistical software such as R and finally to the use of Andromeda. In parallel with the assignments, we saw students' CD begin low and improve.
PB - Virginia Tech
CY - Blacksburg
ER -
TY - RPRT
T1 - Designing for Interactive Dimension Reduction Visual Analytics Tools to Explore High-Dimensional Data
Y1 - 2015
A1 - Self, Jessica Zeitz
A1 - Hu, Xinran
A1 - House, Leanna
A1 - Leman, Scotland
A1 - North, Chris
AB - Exploring high-dimensional data is challenging. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be tweaked to assess the data from multiple perspectives. Alas, tweaking can be difficult for users without a strong knowledge base of the underlying algorithms. In this paper, we present principles for developing interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To exemplify our principles, we introduce an application that implements interactive weighted multidimensional scaling (WMDS). Our application, Andromeda, allows for both parametric and object-level interaction to provide in-depth data exploration. In this paper, we describe the types of tasks and insights that users may gain with Andromeda. Also, the final version of Andromeda is the result of sequential improvements made to multiple designs that were critiqued by users. With each critique we uncovered design principles of effective, interactive, visual analytic tools. These design principles focus on three main areas: (1) layout, (2) semantically visualizing parameters, and (3) designing the communication between the interface and the algorithm.
PB - Virginia Tech
CY - Blacksburg
ER -
TY - RPRT
T1 - Improving Students' Cognitive Dimensionality through Education with Object-Level Interaction
Y1 - 2014
A1 - Self, Jessica Zeitz
A1 - Self, Nathan
A1 - House, Leanna
A1 - Leman, Scotland
A1 - North, Chris
KW - multivariate data analysis
KW - object level interaction
KW - Visual Analytics
AB - This paper addresses the use of visual analytics techniques in education to advance students' cognitive dimensionality. Students naturally tend to characterize data in simplistic one dimensional terms using metrics such as mean, median, mode. Real- world data, however, is more complex and students need to learn to recognize and create high-dimensional arguments. Data exploration methods can encourage thinking beyond traditional one dimensional insights. In particular, visual analytics tools that afford object-level interaction (OLI) allow for generation of more complex insights, despite inexperience with multivariate data. With these tools, students’ insights are of higher complexity in terms of dimensionality and cardinality and built on more diverse interactions. We present the concept of cognitive dimensionality to characterize students' capacity for dimensionally complex insights. Using this concept, we build a vocabulary and methodology to support a student’s progression in terms of growth from low to high cognitive dimensionality. We report findings from a series of classroom assignments with increasingly complex analysis tools. These assignments progressed from spreadsheet manipulations to statistical software such as R and finally to an OLI application, Andromeda. Our findings suggest that students' cognitive dimensionality can be improved and further research on the impact of visual analytics tools on education for cognitive dimensionality is warranted.
PB - Virginia Tech
CY - Blacksburg
ER -
TY - CONF
T1 - StarSpire: Multi-Model Semantic Interaction for Text Analytics
T2 - IEEE Conference on Visual Analytics Science and Technology (VAST)
Y1 - 2014
A1 - Lauren Bradel
A1 - North, Chris
A1 - House, Leanna
A1 - Leman, Scotland
AB - Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection.
JF - IEEE Conference on Visual Analytics Science and Technology (VAST)
PB - IEEE
CY - Paris, France
ER -
TY - JOUR
T1 - Semantics of Directly Manipulating Spatializations
JF - IEEE Transactions on Visualization and Computer Graphics
Y1 - 2013
A1 - Hu, Xinran
A1 - Lauren Bradel
A1 - Maiti, Dipayan
A1 - House, Leanna
A1 - North, Chris
A1 - Leman, Scotland
VL - 19
IS - 12
JO - IEEE Trans. Visual. Comput. Graphics
ER -
TY - JOUR
T1 - Visual to Parametric Interaction (V2PI)
JF - PLoS ONE
Y1 - 2013
A1 - Leman, Scotland
A1 - House, Leanna
A1 - Maiti, Dipayan
A1 - Endert, Alex
A1 - North, Chris
AB - Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as “ Visual to Parametric Interaction” (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.
VL - 8
IS - 3
JO - PLoS ONE
ER -
TY - CONF
T1 - Observation-level interaction with statistical models for visual analytics
T2 - Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on
Y1 - 2011
A1 - Endert, Alex
A1 - Chao Han
A1 - Maiti, Dipayan
A1 - House, Leanna
A1 - Leman, Scotland
A1 - North, Chris
KW - data analysis
KW - data interactive visual exploration
KW - data visualisation
KW - exploratory interaction
KW - expressive interaction
KW - generative topographic mapping
KW - multidimensional scaling
KW - observation-level interaction
KW - parameter adjustments
KW - principal component analysis
KW - probabilistic principal component analysis
KW - probability
KW - sensemaking process
KW - statistical models
KW - Visual Analytics
AB - In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus #x201C;observation #x201D;) in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.
JF - Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on
ER -