Graph-Based Techniques for Visual Analytics of Big Scientific Data

Award Number
NSF IIS-1456763
(Previously NSF IIS-1319363)

Project Duration
8/1/2013-7/31/2018

Principal Investigators
Chaoli Wang (PI), University of Notre Dame
Ching-Kuang Shene (co-PI), Michigan Technological University
Seung Hyun Kim (co-PI), The Ohio State University

Abstract
Scientific visualization has become an indispensable tool for visual analysis of data generated from simulations and experiments across a wide variety of fields. Although there have been substantial advances in developing novel algorithms and techniques for processing, managing and rendering scientific datasets, several critical challenges still remain. These challenges include solving the inherent occlusion and clutter problem when visualizing large three-dimensional scalar and vector fields, examining complex data relationships and tracking their changes over time for time-varying multivariate data, and gaining a comprehensive overview and acquiring full control of data navigation to glean critical insights. The ever-growing size and complexity of data produced only exacerbate these challenges. To enable discovery from big scientific data, there is a need to seek a new perspective on data abstraction and relationship exploration by going beyond the traditional boundary of scientific visualization and fully incorporating information visualization techniques for effective visual data analytics. While there are encouraging, isolated examples of applying information visualization techniques such as parallel coordinates and treemaps to scientific data analysis, leveraging the more generalized and familiar form of graphs to address a wider range of scientific visualization problems at greater extent has not been fully studied. In this project, the PIs' goal is to establish systematic graph-based techniques to investigate large-scale scalar and vector scientific datasets. To this end the team pursues three major tasks: 1) exploring core graph-based techniques to analyze and explore time-varying multivariate scalar and vector field data; 2) developing scalable parallel algorithms for constructing and visualizing large graphs for scientific visualization; and 3) conducting a formal user study using the choice behavior model and tackling real problems from application domains with expert evaluation.

Because scientific visualization plays a key role in many scientific, engineering and medical fields, the potential benefits from generalized graph-based visual analytics tools are far reaching. The general ideas developed will directly benefit the understanding of volumetric scientific datasets including scalar and vector field data, time-varying and multivariate data. They will also impact the understanding of data in other forms such as adaptive mesh refinement, unstructured grid and point-based data. Since this work is a departure from traditional approaches, it could be transformative by providing a completely new way of exploring and analyzing big scientific data. From a scientific perspective, the potential impact is a new class of techniques for knowledge discovery. This project will maximize its outcomes through close collaboration with combustion and biomedical scientists. It will produce results in various forms which are publicized at the project website. Training will be provided for graduate, undergraduate and under-represented students in big data computing and visualization. The role of visualization will be explored in public outreach efforts including summer programs for middle and high school students, and tutorials or contests for researchers at premier visualization conferences.

Collaborators
Peter Brusilovsky, University of Pittsburgh
Nitesh V. Chawla, University of Notre Dame
Yang Chen, Fudan University
Sidney D'Mello, University of Notre Dame
Hanqi Guo, Argonne National Laboratory
Robert Jacob, Argonne National Laboratory
Jingfeng Jiang, Michigan Technological University
David L. Kao, NASA Ames Research Center
Qi Liao, Central Michigan University
Robert J. Nemiroff, Michigan Technological University
Denis Parra, Pontifical Catholic University of Chile
Tom Peterka, Argonne National Laboratory
Feng Qiu, Siemens Corporate Technology
Gökhan Sever, Argonne National Laboratory
Lei Shi, Chinese Academy of Sciences
Daphne Yu, Siemens Corporate Technology
Ye Zhao, Kent State University

Postdoctoral Researchers
Jun Tao, University of Notre Dame

Graduate Students
Yi Gu, University of Notre Dame
Jun Han, University of Notre Dame
Martin Imre, University of Notre Dame
Jun Ma, Michigan Technological University
Jun Tao, Michigan Technological University

Publications

Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gökhan Sever, and Seung Hyun Kim
Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps
IEEE Transactions on Visualization and Computer Graphics (IEEE SciVis 2018), 25(1):1236-1245, Jan 2019.
[PDF](7.3MB) | [WMV](75.3MB)

Jun Tao, Chaoli Wang, Nitesh V. Chawla, Lei Shi, and Seung Hyun Kim
Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields
IEEE Transactions on Visualization and Computer Graphics, 24(12):3200-3213, Dec 2018.
[Selected for Presentation at IEEE VIS 2018]
[PDF](2.8MB) | [WMV](92.1MB)

Martin Imre, Jun Tao, and Chaoli Wang
Identifying Nearly Equally Spaced Isosurfaces for Volumetric Data Sets
Computers & Graphics, 72:82-97, May 2018.
[PDF](3.7MB)

Jun Tao, Lei Shi, Zhou Zhuang, Congcong Huang, Rulei Yu, Purui Su, Chaoli Wang, and Yang Chen
Visual Analysis of Collective Anomalies Through High-Order Correlation Graph
Proceedings of IEEE Pacific Visualization Symposium, Kobe, Japan, pages 150-159, Apr 2018.
[PDF](1.9MB) | [MP4](35.0MB)

Samuel M. Bailey, Justin A. Wei, Chaoli Wang, Denis Parra, and Peter Brusilovsky
CNVis: A Web-Based Visual Analytics Tool for Exploring Conference Navigator Data
Proceedings of IS&T Conference on Visualization and Data Analysis, Burlingame, CA, pages 376-1-376-11, Jan 2018.
[PDF](2.0MB) | [DEMO]

Chaoli Wang
Graph-Based Techniques for Visual Analytics of Scientific Data Sets
IEEE Computing in Science & Engineering, 20(1):93-103, Jan/Feb 2018.
[PDF](2.6MB)

Jian Xu, Jun Tao, Nitesh V. Chawla, and Chaoli Wang
Demo Abstract: Visual Analytics of Higher-Order Dependencies in Sensor Data
Proceedings of ACM/IEEE International Conference on Internet-of-Things Design and Implementation, Pittsburgh, PA, pages 297-298, Apr 2017.
[PDF](651KB)

Jun Tao, Jian Xu, Chaoli Wang, and Nitesh V. Chawla
HoNVis: Visualizing and Exploring Higher-Order Networks
Proceedings of IEEE Pacific Visualization Symposium, Seoul, Korea, pages 1-10, Apr 2017.
[PDF](1.6MB) | [MP4](26.9MB)

Martin Imre, Jun Tao, and Chaoli Wang
Efficient GPU-Accelerated Computation of Isosurface Similarity Maps
Proceedings of IEEE Pacific Visualization Symposium (Visualization Notes), Seoul, Korea, pages 180-184, Apr 2017.
[PDF](1.0MB)

Chaoli Wang and Jun Tao
Graphs in Scientific Visualization: A Survey
Computer Graphics Forum, 36(1):263-287, Jan 2017.
[PDF](3.6MB)

Yi Gu, Chaoli Wang, Robert Bixler, and Sidney D'Mello
ETGraph: A Graph-Based Approach for Visual Analytics of Eye-Tracking Data
Computers & Graphics, 62:1-14, Jan 2017.
[PDF](4.2MB) | [WMV](36.6MB)

Yi Gu, Chaoli Wang, Jun Ma, Robert J. Nemiroff, David L. Kao, and Denis Parra
Visualization and Recommendation of Large Image and Text Collections toward Effective Sensemaking
Information Visualization, 16(1):21-47, Jan 2017.
[PDF](8.9MB) | [WMV](42.5MB)

Ian Turk, Matthew Sinda, Xin'an Zhou, Jun Tao, Chaoli Wang, and Qi Liao
Exploration of Linked Anomalies in Sensor Data for Suspicious Behavior Detection
International Journal of Software and Informatics, 10(3), 10 pages, Oct 2016.
[PDF](947KB) | [WMV](15.4MB)

Jun Tao, Chaoli Wang, Nitesh V. Chawla, and Lei Shi
Semantic Flow Graph: A Framework to Explore 3D Flow Fields
IEEE VIS Poster, Baltimore, MD, Oct 2016.
[PDF](1.4MB) | [WMV](15.6MB)

Jun Tao, Xiaoke Huang, Feng Qiu, Chaoli Wang, Jingfeng Jiang, Ching-Kuang Shene, Ye Zhao, and Daphne Yu
VesselMap: A Web Interface to Explore Multivariate Vascular Data
Computers & Graphics, 59:79-92, Oct 2016.
[PDF](2.8MB) | [WMV](18.7MB)

Yi Gu, Chaoli Wang, Tom Peterka, Robert Jacob, and Seung Hyun Kim
Mining Graphs for Understanding Time-Varying Volumetric Data
IEEE Transactions on Visualization and Computer Graphics (IEEE SciVis 2015), 22(1):965-974, Jan 2016.
[PDF](3.1MB) | [WMV](16.8MB)

Chaoli Wang, John P. Reese, Huan Zhang, Jun Tao, Yi Gu, Jun Ma, and Robert J. Nemiroff
Similarity-Based Visualization of Large Image Collections
Information Visualization, 14(3):183-203, Jul 2015.
[PDF](6.9MB)

Chaoli Wang
A Survey of Graph-Based Representations and Techniques for Scientific Visualization
Eurographics Conference on Visualization - State of The Art Reports, Cagliari, Italy, pages 41-60, May 2015.
[PDF](3.0MB)

Yi Gu, Chaoli Wang, Jun Ma, Robert J. Nemiroff, and David L. Kao
iGraph: A Graph-Based Technique for Visual Analytics of Image and Text Collections
Proceedings of IS&T/SPIE Conference on Visualization and Data Analysis 2015, San Francisco, CA, pages 939708-1-939708-15, Feb 2015.
[Best Paper Award]
[PDF](5.2MB) | [WMV](62.3MB)

Jun Ma, Chaoli Wang, Ching-Kuang Shene, and Jingfeng Jiang
A Graph-Based Interface for Visual Analytics of 3D Streamlines and Pathlines
IEEE Transactions on Visualization and Computer Graphics, 20(8):1127-1140, Aug 2014.
[PDF](4.2MB) | [WMV](45.1MB)

Huan Zhang, Jun Tao, Fang Ruan, and Chaoli Wang
A Study of Animated Transition in Similarity-Based Tiled Image Layout
Tsinghua Science and Technology (Special Issue on Visualization and Computer Graphics), 18(2):157-170, Apr 2013.
[PDF](3.8MB) | [WMV](33.5MB)

Jun Ma, Chaoli Wang, and Ching-Kuang Shene
FlowGraph: A Compound Hierarchical Graph for Flow Field Exploration
Proceedings of IEEE Pacific Visualization Symposium, Sydney, Australia, pages 233-240, Feb 2013.
[Honorable Mention]
[PDF](2.5MB) | [WMV](37.6MB)

Yi Gu and Chaoli Wang
iTree: Exploring Time-Varying Data Using Indexable Tree
Proceedings of IEEE Pacific Visualization Symposium, Sydney, Australia, pages 137-144, Feb 2013.
[PDF](2.0MB) | [WMV](40.1MB)

Chaoli Wang, John P. Reese, Huan Zhang, Jun Tao, and Robert J. Nemiroff
iMap: A Stable Layout for Navigating Large Image Collections with Embedded Search
Proceedings of IS&T/SPIE Conference on Visualization and Data Analysis, Burlingame, CA, pages 86540K-1-86540K-14, Feb 2013.
[Best Paper Award]
[PDF](4.1MB) | [WMV](34.8MB)

Outreach

Chaoli Wang
Graphs in Scientific Visualization: Past, Present, and Future (Guest Lecture)
Visualization Summer School, Peking University, Jul 10 and 11, 2017

Chaoli Wang
A Statistical Approach to Geometry-Based Flow Visualization (Invited Talk)
Institute of Software, Chinese Academy of Sciences, Jul 11, 2017
Fuzhou University, Jul 5, 2017
Shandong University, Jun 30, 2017
ACMS Statistics Seminar, University of Notre Dame, Apr 4, 2017

Jun Tao, Hanqi Guo, Bei Wang, Christoph Garth, and Tino Weinkauf
Recent Advancements of Feature-Based Flow Visualization and Analysis (Conference Tutorial)
IEEE VIS Tutorial 2016, Baltimore, MD, Oct 23, 2016
[HTM]

Chaoli Wang
The Role of Graphs in Scientific Visualization: From Visual Interface to Analytics Tool (Invited Talk)
Pontifical Catholic University of Chile, Mar 9, 2016
Western Michigan University, Oct 8, 2015

Chaoli Wang
A Survey of Graph-Based Representations and Techniques for Scientific Visualization (Conference Talk)
Eurographics Conference on Visualization, Cagliari, Italy, May 27, 2015

Chaoli Wang
Visual Analytics of Big Scientific Data Using Graphs (Distance Learning Lecture)
MSIM742/842: Visualization II, Old Dominion University, Feb 10, 2015

Chaoli Wang
Visual Analytics of Scientific Data Using Graph-Based Techniques (Invited Talk)
SUNY Korea, May 23, 2014
Sejong University, May 15, 2014

Chaoli Wang
Flow Visualization in the Era of Big Data (Distance Learning Lecture)
MSIM742/842: Visualization II, Old Dominion University, Mar 18, 2014

Chaoli Wang, Han-Wei Shen, Daniel Weiskopf, Tom Peterka, and Guoning Chen
State-of-the-Art Flow Field Analysis and Visualization (Conference Tutorial)
IEEE VIS Tutorial 2013, Atlanta, GA, Oct 13, 2013
[HTM]

Chaoli Wang
Gaining Insights from Analysis and Visualization of Big Scientific Data (Invited Talk)
University of Notre Dame, Oct 3, 2013

Notables

Aug 1, 2018. Dr. Jun Tao will start as an Associate Professor at Sun Yat-sen University in October 2018. Congratulations, Dr. Tao!

May 3, 2016. PhD student Yi Gu successfully defended his PhD dissertation. Congratulations, Dr. Gu!

Apr 28, 2016. PhD student Yi Gu for won an Outstanding Research Assistant Award from Notre Dame's CSE department.

Oct 25, 2015. PhD student Yi Gu won a student volunteer position at IEEE VIS, October 25-30, 2015, in Chicago, Illinois.

Oct 19, 2015. PhD student Jun Tao successfully defended his PhD dissertation. Congratulations, Dr. Tao!

Mar 23, 2015. PhD student Jun Tao won a Finishing Fellowship for the Summer 2015 semester from Michigan Tech's Graduate School. His dissertation title is "Enabling Techniques for Expressive Flow Field Visualization and Exploration."

Feb 6, 2015. PhD student Jun Tao won a Dean's Award for Outstanding Scholarship from Michigan Tech Graduate School.

Dec 12, 2014. PhD student Jun Ma successfully defended his PhD dissertation. Congratulations, Dr. Ma!

Dec 10, 2014. Our iGraph paper will receive a Best Paper Award at the IS&T/SPIE Conference on Visualization and Data Analysis (VDA), February 9-11, 2015, in San Francisco, California. [HTM]

Nov 13, 2013. PhD student Jun Ma won a Finishing Fellowship for the Spring 2014 semester from Michigan Tech's Graduate School. Jun Ma is the first PhD student in the CS department to receive this award. His dissertation title is "Analysis and Visualization of Flow Fields using Information-Theoretic Techniques and Graph-Based Representations." [HTM]

Mar 1, 2013. Our FlowGraph paper received an Honorable Mention at IEEE Pacific Visualization Symposium (PacificVis), February 26-March 1, 2013, in Sydney, Australia. IEEE PacificVis is one of the three leading conferences in the field of visualization. [HTM]

Dec 21, 2012. Our iMap paper will receive a Best Paper Award at the IS&T/SPIE Conference on Visualization and Data Analysis (VDA), February 3-6, 2013, in Burlingame, California. [HTM]


This material is based upon work supported by the National Science Foundation under Grant No. 1456763 (previously 1319363). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Page Created on 8/8/2013, Last Updated on 8/1/2018