Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.

[1]  Daniel A. Keim,et al.  Knowledge Generation Model for Visual Analytics , 2014, IEEE Transactions on Visualization and Computer Graphics.

[2]  Silvia Miksch,et al.  Gnaeus: Utilizing Clinical Guidelines for Knowledge-assisted Visualisation of EHR Cohorts , 2015, EuroVA@EuroVis.

[3]  Min Chen,et al.  From Web Data to Visualization via Ontology Mapping , 2008, Comput. Graph. Forum.

[4]  John T. Stasko,et al.  Distributed Cognition as a Theoretical Framework for Information Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[5]  Chaomei Chen,et al.  Top 10 Unsolved Information Visualization Problems , 2005, IEEE Computer Graphics and Applications.

[6]  Wolfgang Aigner,et al.  Comparative Evaluation of an Interactive Time‐Series Visualization that Combines Quantitative Data with Qualitative Abstractions , 2012, Comput. Graph. Forum.

[7]  Heidrun Schumann,et al.  A systematic view on data descriptors for the visual analysis of tabular data , 2017, Inf. Vis..

[8]  Silvia Miksch,et al.  Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation , 2011, USAB.

[9]  Ben Shneiderman,et al.  LifeLines: using visualization to enhance navigation and analysis of patient records , 1998, AMIA.

[10]  Yuval Shahar,et al.  Intelligent visualization and exploration of time-oriented clinical data , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[11]  Wolfgang Aigner,et al.  A knowledge-assisted visual malware analysis system: Design, validation, and reflection of KAMAS , 2016, Comput. Secur..

[12]  Markus Wagner,et al.  KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis , 2017, IEEE Transactions on Visualization and Computer Graphics.

[13]  Margaret-Anne D. Storey,et al.  Creating Visualizations through Ontology Mapping , 2009, 2009 International Conference on Complex, Intelligent and Software Intensive Systems.

[14]  David Kirsh,et al.  Thinking with external representations , 2010, AI & SOCIETY.

[15]  William Ribarsky,et al.  The Human-Computer System: Towards an Operational Model for Problem Solving , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[16]  Daniel A. Keim,et al.  Mastering the Information Age - Solving Problems with Visual Analytics , 2010 .

[17]  Jarke J. van Wijk,et al.  The value of visualization , 2005, VIS 05. IEEE Visualization, 2005..

[18]  Daniel A. Keim,et al.  Viewing Visual Analytics as Model Building , 2018, Comput. Graph. Forum.

[19]  Jean-Daniel Fekete,et al.  Visual Analytics Infrastructures: From Data Management to Exploration , 2013, Computer.

[20]  Ben Shneiderman,et al.  Extracting Insights from Electronic Health Records: Case Studies, a Visual Analytics Process Model, and Design Recommendations , 2011, Journal of Medical Systems.

[21]  Miriah D. Meyer,et al.  A Framework for Externalizing Implicit Error Using Visualization , 2019, IEEE Transactions on Visualization and Computer Graphics.

[22]  Sigmar-Olaf Tergan,et al.  Visualizing Knowledge and Information: An Introduction , 2005, Knowledge and Information Visualization.

[23]  Gennady L. Andrienko,et al.  Exploratory analysis of spatial and temporal data - a systematic approach , 2005 .

[24]  Min Chen,et al.  Data, Information, and Knowledge in Visualization , 2009, IEEE Computer Graphics and Applications.

[25]  Otto-von-Guericke.  Connecting Time-Oriented Data and Information to a Coherent Interactive Visualization , 2004 .

[26]  Silvia Miksch,et al.  Towards a Concept how the Structure of Time can Support the Visual Analytics Process , 2011, EuroVA@EuroVis.

[27]  Silvia Miksch,et al.  The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics , 2017, 2017 IEEE Conference on Visual Analytics Science and Technology (VAST).

[28]  Tamara Munzner,et al.  Design Study Methodology: Reflections from the Trenches and the Stacks , 2012, IEEE Transactions on Visualization and Computer Graphics.

[29]  Yuval Shahar,et al.  Exploration of patterns predicting renal damage in patients with diabetes type II using a visual temporal analysis laboratory , 2015, J. Am. Medical Informatics Assoc..

[30]  Christian Tominski,et al.  Event-Based Concepts for User-Driven Visualization , 2011, Inf. Vis..

[31]  John T. Stasko,et al.  The Science of Interaction , 2009, Inf. Vis..

[32]  Jason Dykes,et al.  Action Design Research and Visualization Design , 2016, BELIV '16.

[33]  William Ribarsky,et al.  Defining and applying knowledge conversion processes to a visual analytics system , 2009, Comput. Graph..

[34]  Hercules Dalianis.  Medical Classifications and Terminologies , 2018, Clinical Text Mining.

[35]  Arvind Satyanarayan,et al.  Vega-Lite: A Grammar of Interactive Graphics , 2018, IEEE Transactions on Visualization and Computer Graphics.

[36]  David Gotz,et al.  Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization , 2012, IEEE Transactions on Visualization and Computer Graphics.

[37]  Silvia Miksch,et al.  Qualizon graphs: space-efficient time-series visualization with qualitative abstractions , 2014, AVI.

[38]  Antoine Isaac,et al.  SKOS Simple Knowledge Organization System Primer , 2009 .

[39]  Peter Wegner,et al.  Why interaction is more powerful than algorithms , 1997, CACM.

[40]  Wolfgang Aigner,et al.  KAVA-Time: Knowledge-Assisted Visual Analytics Methods for Time-Oriented Data , 2018 .

[41]  Daniel A. Keim,et al.  Visual Analytics: Scope and Challenges , 2008, Visual Data Mining.

[42]  Pierre-Antoine Champin,et al.  JSON-LD 1.1 – A JSON-based Serialization for Linked Data , 2019 .

[43]  Yuval Shahar,et al.  Intelligent visualization and exploration of time-oriented data of multiple patients , 2010, Artif. Intell. Medicine.