Big Data Visualization and Analytics: Future Research Challenges and Emerging Applications

In the context of data visualization and analytics, this report outlines some of the challenges and emerging applications that arise in the Big Data era. In particularly, fourteen distinguished scientists from academia and industry, and diverse related communities, i.e., Information Visualization, Human-Computer Interaction, Machine Learning, Data management & Mining, and Computer Graphics have been invited to express their opinions.

[1]  Antti Oulasvirta,et al.  Towards Perceptual Optimization of the Visual Design of Scatterplots , 2017, IEEE Transactions on Visualization and Computer Graphics.

[2]  George Papastefanatos,et al.  Linked Data Visualization: Techniques, Tools, and Big Data , 2020, Linked Data Visualization.

[3]  Timos K. Sellis,et al.  Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art , 2016, EDBT/ICDT Workshops.

[4]  Guoliang Li,et al.  Making data visualization more efficient and effective: a survey , 2019, The VLDB Journal.

[5]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[6]  Ulrik Brandes,et al.  Quality Metrics for Information Visualization , 2018, Comput. Graph. Forum.

[7]  Steven M. Drucker,et al.  Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models , 2019, CHI.

[8]  Hanspeter Pfister,et al.  Commercial Visual Analytics Systems–Advances in the Big Data Analytics Field , 2019, IEEE Transactions on Visualization and Computer Graphics.

[9]  Jessica Hullman,et al.  Why Authors Don't Visualize Uncertainty , 2019, IEEE Transactions on Visualization and Computer Graphics.

[10]  Kwan-Liu Ma,et al.  A Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data , 2018, IEEE Transactions on Visualization and Computer Graphics.

[11]  Jean-Daniel Fekete,et al.  Progressive Data Analysis and Visualization , 2019 .

[12]  Stefan Thalmann,et al.  Data Analytics for Industrial Process Improvement A Vision Paper , 2018, 2018 IEEE 20th Conference on Business Informatics (CBI).

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

[14]  Yang Wang,et al.  Privacy Preserving Visualization: A Study on Event Sequence Data , 2018, Comput. Graph. Forum.

[15]  Kwan-Liu Ma,et al.  Visualizing Flow of Uncertainty through Analytical Processes , 2012, IEEE Transactions on Visualization and Computer Graphics.

[16]  Antti Oulasvirta,et al.  Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation , 2019, Cogn. Sci..

[17]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[18]  Jing Yang,et al.  Guidance in the human-machine analytics process , 2018, Vis. Informatics.

[19]  Martin Raubal,et al.  Eye tracking support for visual analytics systems: foundations, current applications, and research challenges , 2019, ETRA.

[20]  David Maxwell Chickering,et al.  ModelTracker: Redesigning Performance Analysis Tools for Machine Learning , 2015, CHI.

[21]  Jeffrey Heer,et al.  ReVision: automated classification, analysis and redesign of chart images , 2011, UIST.

[22]  Daniel A. Keim,et al.  Visual Analytics of Movement , 2013, Springer Berlin Heidelberg.

[23]  John Allen,et al.  Scuba: Diving into Data at Facebook , 2013, Proc. VLDB Endow..

[24]  Kwan-Liu Ma,et al.  Uncertainty-Aware Visualization for Analyzing Heterogeneous Wildfire Detections , 2019, IEEE Computer Graphics and Applications.

[25]  Kwan-Liu Ma,et al.  Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning , 2019, IEEE Transactions on Visualization and Computer Graphics.

[26]  Ben Shneiderman,et al.  Response time and display rate in human performance with computers , 1984, CSUR.

[27]  Johannes Gehrke,et al.  Accurate intelligible models with pairwise interactions , 2013, KDD.

[28]  Stefan Wrobel,et al.  Constructing Spaces and Times for Tactical Analysis in Football , 2019, IEEE Transactions on Visualization and Computer Graphics.

[29]  Jean-Daniel Fekete,et al.  Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis , 2016, ArXiv.

[30]  Ross Maciejewski,et al.  Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions , 2017, IEEE Transactions on Intelligent Transportation Systems.

[31]  M. Sheelagh T. Carpendale,et al.  Theoretical analysis of uncertainty visualizations , 2006, Electronic Imaging.

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

[33]  Kwan-Liu Ma,et al.  What Would a Graph Look Like in this Layout? A Machine Learning Approach to Large Graph Visualization , 2017, IEEE Transactions on Visualization and Computer Graphics.

[34]  Kwan-Liu Ma,et al.  A framework for uncertainty-aware visual analytics , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[35]  Jeffrey Heer,et al.  Reverse‐Engineering Visualizations: Recovering Visual Encodings from Chart Images , 2017, Comput. Graph. Forum.

[36]  Silvia Miksch,et al.  Characterizing Guidance in Visual Analytics , 2017, IEEE Transactions on Visualization and Computer Graphics.

[37]  Kwan-Liu Ma,et al.  GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms , 2019, IEEE Transactions on Visualization and Computer Graphics.

[38]  Younghoon Kim,et al.  GraphScape: A Model for Automated Reasoning about Visualization Similarity and Sequencing , 2017, CHI.

[39]  Jarek Gryz,et al.  Interactive Visualization of Large Data Sets , 2016, IEEE Transactions on Knowledge and Data Engineering.

[40]  Ying Zhao,et al.  A survey of visualization for smart manufacturing , 2018, Journal of Visualization.

[41]  Christoph Trattner,et al.  VizRec , 2016 .

[42]  Yang Wang,et al.  Revealing the fog-of-war: A visualization-directed, uncertainty-aware approach for exploring high-dimensional data , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[43]  Jeffrey Heer,et al.  Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco , 2018, IEEE Transactions on Visualization and Computer Graphics.

[44]  Alex Endert,et al.  Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication , 2019, IEEE Transactions on Visualization and Computer Graphics.

[45]  Jeffrey Heer,et al.  Beyond Heuristics: Learning Visualization Design , 2018, ArXiv.

[46]  Jaegul Choo,et al.  Visual Analytics for Explainable Deep Learning , 2018, IEEE Computer Graphics and Applications.

[47]  Kwan-Liu Ma,et al.  A Deep Generative Model for Graph Layout , 2019, IEEE Transactions on Visualization and Computer Graphics.

[48]  Carsten Binnig,et al.  Progressive Data Science: Potential and Challenges , 2018, ArXiv.