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 - 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 -