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 - Observation-Level Interaction with Clustering and Dimension Reduction Algorithms
T2 - Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics
Y1 - 2017
A1 - Wenskovitch, John
A1 - North, Chris
KW - data clustering
KW - Observation-Level Interaction (OLI)
KW - Semantic interaction
KW - sensemaking
KW - Visual Analytics
JF - Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics
T3 - HILDA'17
PB - ACM
CY - New York, NY, USA
SN - 978-1-4503-5029-7
UR - http://doi.acm.org/10.1145/3077257.3077259
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 -
TY - CONF
T1 - An ordering of secondary task display attributes
T2 - CHI '02: CHI '02 extended abstracts on Human factors in computing systems
Y1 - 2002
A1 - Tessendorf, David
A1 - Chewar, C. M.
A1 - Ndiwalana, Ali
A1 - Pryor, Jon
A1 - McCrickard, D. Scott
A1 - North, Chris
JF - CHI '02: CHI '02 extended abstracts on Human factors in computing systems
PB - ACM
CY - New York, NY, USA
SN - 1-58113-454-1
ER -