A Bidirectional Pipeline for Semantic Interaction

Semantic interaction techniques in visual analytics tools allow analysts to indirectly adjust model parameters by directly manipulating the visual output of the models. Many existing tools that support semantic interaction do so with a number of similar features, including using a set of mathematical models that are composed within a pipeline, having a semantic interaction be interpreted by an inverse computation of one or more mathematical models, and using an underlying bidirectional structure within the pipeline. We propose a new visual analytics pipeline that captures these necessary features of semantic interactions. To demonstrate how this pipeline can be used, we represent existing visual analytics tools and their semantic interactions within this pipeline. We also explore a series of new visual analytics tools with semantic interaction to highlight how the new pipeline can represent new research as well.

[1]  Chris North,et al.  Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering , 2012, IEEE Transactions on Visualization and Computer Graphics.

[2]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[3]  Chris North,et al.  SIRIUS: Dual, Symmetric, Interactive Dimension Reductions , 2019, IEEE Transactions on Visualization and Computer Graphics.

[4]  Mario Costa Sousa,et al.  iLAMP: Exploring high-dimensional spacing through backward multidimensional projection , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[5]  Luis Gustavo Nonato,et al.  Local Affine Multidimensional Projection , 2011, IEEE Transactions on Visualization and Computer Graphics.

[6]  Chris North,et al.  Observation-Level Interaction with Clustering and Dimension Reduction Algorithms , 2017, HILDA@SIGMOD.

[7]  Alex Endert,et al.  Podium: Ranking Data Using Mixed-Initiative Visual Analytics , 2018, IEEE Transactions on Visualization and Computer Graphics.

[8]  Daniel A. Keim,et al.  Visual Analytics: Definition, Process, and Challenges , 2008, Information Visualization.

[9]  Chris North,et al.  Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics , 2018, IEEE Transactions on Visualization and Computer Graphics.

[10]  James Fogarty,et al.  Regroup: interactive machine learning for on-demand group creation in social networks , 2012, CHI.

[11]  Dorota Glowacka,et al.  Directing exploratory search with interactive intent modeling , 2013, CIKM.

[12]  Lars Linsen,et al.  Interactive Design of Multidimensional Data Projection Layout , 2014, EuroVis.

[13]  Steven M. Drucker,et al.  Helping Users Sort Faster with Adaptive Machine Learning Recommendations , 2011, INTERACT.

[14]  Chao Han,et al.  Bayesian visual analytics: BaVA , 2015, Stat. Anal. Data Min..

[15]  Chris North,et al.  Observation-level interaction with statistical models for visual analytics , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[16]  P. Pirolli,et al.  The Sensemaking Process and Leverage Points for Analyst Technology as Identified Through Cognitive Task Analysis , 2007 .

[17]  C. North,et al.  Visual to Parametric Interaction (V2PI) , 2013, PloS one.

[18]  Carla E. Brodley,et al.  Dis-function: Learning distance functions interactively , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[19]  Luis Gustavo Nonato,et al.  User‐driven Feature Space Transformation , 2013, Comput. Graph. Forum.

[20]  Analyst's Workspace: An embodied sensemaking environment for large, high-resolution displays , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[21]  Christopher Andrews,et al.  Space to think: large high-resolution displays for sensemaking , 2010, CHI.

[22]  Charl P. Botha,et al.  Piece wise Laplacian‐based Projection for Interactive Data Exploration and Organization , 2011, Comput. Graph. Forum.

[23]  Alex Endert,et al.  InterAxis: Steering Scatterplot Axes via Observation-Level Interaction , 2016, IEEE Transactions on Visualization and Computer Graphics.

[24]  Alex Endert,et al.  Semantic Interaction for Visual Analytics: Toward Coupling Cognition and Computation , 2014, IEEE Computer Graphics and Applications.

[25]  Designing Usable Interactive Visual Analytics Tools for Dimension Reduction , 2016 .

[26]  Chris North,et al.  Bridging the gap between user intention and model parameters for human-in-the-loop data analytics , 2016, HILDA '16.

[27]  Chris North,et al.  Multi-model semantic interaction for text analytics , 2014, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST).

[28]  Wei Chen,et al.  A Survey of Visual Analytic Pipelines , 2016, Journal of Computer Science and Technology.

[29]  Chris North,et al.  Semantic interaction for visual text analytics , 2012, CHI.

[30]  Frank M. Shipman,et al.  Formality Considered Harmful: Experiences, Emerging Themes, and Directions on the Use of Formal Representations in Interactive Systems , 1999, Computer Supported Cooperative Work (CSCW).

[31]  Alex Endert,et al.  AxiSketcher: Interactive Nonlinear Axis Mapping of Visualizations through User Drawings , 2017, IEEE Transactions on Visualization and Computer Graphics.