Visual Analysis of Scientific Phenomena
by Thomas Ertl (University of Stuttgart)

While Visual Analytics was initially understood as integrating advanced data mining with information visualization techniques, we now see more and more scientific visualization applications with sophisticated data analysis parts and highly linked spatial and non-spatial views. This talk will present some recent projects in this domain from the University of Stuttgart dealing with solar magnetic fields and two-phase flows.

Biography

Thomas Ertl received a MSc in computer science from the University of Colorado at Boulder and a PhD in theoretical astrophysics from the University of Tübingen. Since 1999 he is a full professor of computAer science at the University of Stuttgart now leading the Visualization and Interactive Systems Institute (VIS) and the Visualization Research Center of the University of Stuttgart (VISUS). His research interests include visualization, computer graphics and human computer interaction in general with a focus on volume rendering, flow and particle visualization, parallel and hardware accelerated graphics, large datasets and interactive steering, visual analytics of text collections and social media, user interfaces and navigation systems for the blind. Dr. Ertl is coauthor of more than 400 scientific publications and he served on numerous program committees and as a papers cochair for most conferences in the field. From 2007-2010 Dr. Ertl was Editor-in-Chief of the IEEE Transactions on Visualization and Graphics (TVCG) and in 2011/2012 he served as Chairman of the Eurographics Association. He received the Outstanding Technical Contribution Award of the Eurographics Association and the Technical Achievement Award of the IEEE Visualization and Graphics Technical Committee in 2006. In 2007 he was elected as a Member of the Heidelberg Academy of Sciences and Humanities. He received Honorary Doctorates from the Vienna University of Technology in 2011 and from the University of Magdeburg in 2014.

Symmetry in Scalar Fields: An Approach to Query based Exploration
by Vijay Natarajan (Indian Institute of Science)

Several natural and man-made objects exhibit symmetry in different forms, both in their geometry and in the material distribution. The study of symmetry plays an important role in understanding both the structure of these objects and their physical properties. In this talk, I will introduce the problem of symmetry detection in a scalar field, a real-valued function defined on a spatial domain of interest. The goal is to identify regions within the domain of a scalar field that remain invariant under transformations of both domain geometry and the scalar values. Symmetry detection in scientific data is still a nascent area of research and existing methods that detect symmetry are either not robust in the presence of noise or are computationally costly.

I will present recently developed methods that detect symmetry in, potentially noisy, 3D scalar fields. The key ingredient of each algorithm is a data structure that captures topological and geometric properties of the scalar field. I will demonstrate applications to symmetry-aware volume visualization, volume editing, linked selection, and in particular discuss how symmetry detection enables query-based exploration of feature-rich data. (http://vgl.serc.iisc.ernet.in)

Biography

Vijay Natarajan is an associate professor in the Department of Computer Science and Automation and the Supercomputer Education and Research Centre at the Indian Institute of Science, Bangalore. He received the Ph.D. degree in computer science from Duke University in 2004 and holds the B.E. degree in computer science and M.Sc. degree in mathematics from Birla Institute of Technology and Science, Pilani, India. His research interests include scientific visualization, computational geometry, and computational topology.

http://www.csa.iisc.ernet.in/~vijayn

http://vgl.serc.iisc.ernet.in

Visual Text Analytics: Connecting Big Data with People
by Shixia Liu (Tsinghua University)

Text data such as news articles and Twitter posts, are considered an important component of big data. As a result, analyzing text documents has become increasingly an important part of decision making in large corporations and small businesses. In this talk, I will present the challenges of visual text analytics and exemplifies them with several text visualization techniques and examples. It aims at investigating how to effectively present interesting topics and their evolution over time to humans in an understandable and manageable manner so that user interactive exploration and analytical reasoning can be supported. This is the core task of connecting big data with people.

Biography

Shixia Liu is an associate professor in the School of Software, Tsinghua University. Her research interest includes visual text analytics, visual behavior analytics, graph visualization, and text mining. She received a B.S. and M.S. in Computational Mathematics from Harbin Institute of Technology, a Ph.D. in Computer Aided Design and Computer Graphics from Tsinghua University. Before She joined Tsinghua, She worked as a lead researcher at Microsoft Research Asia and a research staff member and research manager at IBM China Research Lab.

• Jiawan Zhang (Tianjin University)
• Jie Liang (Peking University)
• Ronghua Liang (Zhejiang University of Technology)
• Wei Chen (Zhejiang University)

Data Exploration and Presentation with Natural User Interfaces
by Bongshin Lee (Microsoft Research)

Over the last decade, we have observed rapid advances and innovations in the Natural User Interface (NUI) space, witnessing constant emergence of new modalities for interacting with computing systems. Yet, visualization systems are still largely designed for the classic desktop setup with mouse and keyboard. Leveraging these advances in NUI (specifically pen and touch), I have been investigating new, more natural ways of accessing information that helps people efficiently explore and present their data. In this talk, I will showcase two interactive visualization systems that go beyond the mouse and keyboard, providing more effective and engaging methods of exploring, manipulating, and presenting data.

Biography

Bongshin Lee is a Researcher in the neXus group at Microsoft Research (MSR). Her research interests include Human-Computer Interaction, Information Visualization, and User Interfaces and Interaction Techniques. She designs and develops profoundly innovative ways for people to create information visualizations, interact with their data, and share data stories visually leveraging Natural User Interfaces (NUIs) including pen and touch. Before joining MSR, Bongshin earned her M.S. and Ph.D. degrees in Computer Science from University of Maryland at College Park in 2002 and 2006, respectively.

Visual Analytics of Big Complex Networks
by Seokhee Hong (University of Sydney)

Recent technological advances have led to big complex network models in many domains, including social networks and biological networks. Good visualisation can reveal the hidden structure of the networks and amplifies human understanding, thus leading to new insights and findings. However, visualisation of massive complex networks is challenging due to scalability and complexity.

This talk will address the challenging issues for visual analytics of big complex networks, and review latest methods for visual analytics of such networks.

Biography

Seok-Hee Hong is a professor and a Future Fellow at the University of Sydney. Her research interests include Graph Drawing, Algorithms, Information Visualisation and Visual Analytics. She serves as a Steering Committee member of Graph Drawing Symposium, IEEE Pacificvis Symposium, and ISAAC (International Symposium on Algorithms and Computation), and an editor of JGAA (Journal of Graph Algorithms and Applications) and IEEE CGA (Computer Graphics and Applications).

Influence Graph Visualization: Technique and Demonstration
by Lei Shi (Institute of Software, CAS)

Visually analyzing influence graphs, such as those in the citation network, poses challenge to many fields of the computer science research. How can we summarize a large graph according to user's interest? In particular, how can we illustrate the impact of a highly influential paper through the summarization? Can we maintain the sensory node-link visual metaphor while preserving both the influence flow patterns and fine readability? The state-of-the-art graph compression algorithms exploit the redundancy of graph structure, however produce huge visual clutter when applied on complex influence networks. On the other hand, existing graph summarization methods fold the large graph into clustered views, but fail to reveal the hidden influence structure underneath the network. In this work, we first formally define the influence graph summarization problem on information networks. Second, we present VEGAS, an end-to-end framework to solve this new problem. Our method can not only highlight the flow-based influence patterns through the visual summarization, but also inherently support rich attribute information. Third, we propose an analysis of our matrix decomposition based algorithm and theoretically prove its rationality in approximating the objective of the proposed problem. Last, we conduct comprehensive experiments with real-world citation networks to compare our method with classical graph summarization algorithms. Evaluation results demonstrate that our method significantly outperforms previous ones in optimizing both the quantitative objective and the quality of visual summarizations.

Biography

Lei Shi is an associate research professor in the State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences. Before 2012, he was a staff researcher, research staff member and research manager at IBM Research - China, working on information visualization and visual analytics. He holds B.S. (2003), M.S. (2006) and Ph.D. (2008) degrees from Department of Computer Science and Technology, Tsinghua University. His research interests span Information Visualization, Visual Analytics, Data Mining and Networked Systems. He has published more than 50 papers in refereed conferences and journals. He is the recipient of IBM Research Accomplishment Award on "Visual Analytics" and the VAST Challenge Award twice in 2010 and 2012.

Applied network security visualisation
by Mengyang Jiang (Qihu Inc.)

• From log entries to visualisation: how did the transition happen?
• Demonstration of the DNS traffic visual analytic system
• Demonstration of the internal network access visualization
• The architecture and implementation of complex data visualisation applications

Mengyang Jiang is from Skyeye Team, Qihoo 360 Technology Co Ltd. He is a Data visualisation engineer.

Development of Data Visualization in Chinese Media: the Case of Caixin Data Visualization Lab
by Huang Zhiming (Caixin Inc.)

Caixin Data Visualization Lab was set up in October 2013. It is a virtual laboratory exploring the synergy of news production and data research development. Being dedicated to data journalism and data visualization, the team has published several award-winning projects and is highly recognized in the industry.

In November 2013, the Lab published the Serial reports of Oil pipeline blast in Qingdao: Real-time photo album, which was awarded the Excellence in Reporting Breaking News 2014 by Society of Publishers in Asia (SOPA). This is the first time coding developers receive a journalism award. In the project, the team had fully utilized different kinds of technology to digest and present the information collected by the frontline reporters. For example, GPS system on mobile phones was employed to locate the photos.

The team has brought to the public more than 50 projects, including the extensive investigative report on the former Politburo Standing Committee Zhou Yongkang, Zhou’s Power Base (caixin.com/zyk). By converting news articles of 60 thousand words into an interactive webpage, the complex interest web of Zhou is presented in a clear and user-friendly way. With this project, the team received the Award of Excellence in 2014 Best of Digital Design competition organized by the Society of News Design, SND. Locally, the project also received “Data Journalism of the Year” by Tencent and “Work of Multimedia of the Year” by NetEase.

Biography

Mr Huang Zhimin, CTO of Caixin Media and director of Caixin Data Visualization Lab, has more then 10 years of experience in Internet and mobile Internet industry. He is now dedicated in promoting the news media development with the power of big data and data visualization. Mr Huang holds seminars and talks in universities and media sectors, including Peking University, Tsinghua University, Renmin University, Communication University of China, Fudan University, East China Normal University, Wuhan University, Xiamen University, Xinhua News Agency, People’s Daily and Sina.com.

Taobao Data visualization for Business Research
by Xiao Wen (Ali Inc.)

Visual Analytics for Multivariate Data Analytics with Application to Climate Science Research
by Klaus Mueller (Stony Brook University)

The growth of digital data is tremendous. Any aspect of life and matter is being recorded and stored on cheap disks, either in the cloud, in businesses, or in research labs. We can now afford to explore very complex relationships with many variables playing a part. But for this we need powerful tools that allow us to be creative, to sculpt this intricate insight formulated as models from the large raw block of data. High quality visual feedback plays a decisive role here. In this talk, I will first provide some fundamental insight into the peculiarities of high-dimensional data spaces and will then discuss our recent software framework, called “The ND-Scope”, which incorporates various facilities for high-dimensional data exploration and reasoning with high-dimensional data, also in the context of the earth’s geography. The ND-Scope was conceived in tight collaborations with domain users from climate and environmental science at BNL and PNL. I will present use cases resulting from these studies.

Biography

Klaus Mueller received a PhD in computer science from the Ohio State University. He is currently a professor in the Computer Science Department at Stony Brook University and the chair of the Computer Science Department at SUNY Korea. His current research interests are computer graphics, visual analytics, medical imaging, and high-performance computing, He won the US National Science Foundation CAREER award in 2001 and the SUNY Chancellor Award in 2011. Mueller has authored more than 160 peer-reviewed journal and conference papers, which have been cited more than 6,000 times. He is a frequent speaker at international conferences, has participated in 15 tutorials on various topics, and is a currently the chair of the IEEE Technical Committee on Visualization and Computer Graphics. He was an associate editor of IEEE Transactions on Visualization and Computer Graphics and is a senior member of the IEEE. For more information, please see http://www.cs.sunysb.edu/~mueller

Visual Behavior Analytics
by Yingcai Wu (Zhejiang University)

Online service providers, such as Twitter, Amazon, Google, and Wikipedia, generate huge volumes of user behavior data on a daily basis, where valuable patterns and correlations of user behaviors are hidden. For companies, effective analysis of the behavior data allows them to learn more about their customers on an unprecedented scale to improve customer relations and develop social media marketing strategies. For governments, effective tracking of the behavior data allows them to detect and predict critical events to make proper decisions in a timely manner. However, analysis of the behavior data is challenging due to the enormous amount of data and the heterogeneity of information. Visual behavior analytics has recently emerged as a new research topic in visualization and visual analytics for exploring massive and heterogeneous user behavior data. In my talk, I will discuss the challenges and opportunities of the research on visual behavior analytics, and then give some examples of applying interactive visualization techniques to making sense of the behavior data.

Biography

Yingcai Wu is an assistant professor at the State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China. He received his Ph.D. degree in Computer Science from The Hong Kong University of Science and Technology (HKUST), Hong Kong in 2009 and obtained his B.Eng. degree in Computer Science and Technology from South China University of Technology, Guangzhou, China in 2004. Prior to his current position, Yingcai Wu was a researcher at the Internet Graphics Group in Microsoft Research Asia, Beijing, China from May 2012 to January 2015. He was a postdoctoral researcher at the Visualization research group in HKUST from January 2010 to May 2010, and at the Visualization and interface Design Innovation (VIDi) research group in the University of California, Davis from May 2010 to March 2012. His primary research interests lie in visual behavior analytics, visual analytics of social media, visual text analytics, uncertainty-aware visual analytics, and information visualization. For more information, visit www.ycwu.org

TBD
by Maolin Huang (University of Technology in Sydney)

Towards music visual analytics
by Takayuki Itoh (Ochanomizu University)

Visualization is a good tool to quickly understand the contents of music rather than taking a time to listen to. Many studies on music visualization have been discussed both in information visualization and music information retrieval communities. This talk briefly introduces systematics of music visualization techniques and speaker's own studies. This talk also discusses how visual analytics framework can be contributed to music analytics.

Biography

Takayuki Itoh is a full professor of Ochanomizu University, Japan. He received B.S., M.S., and Ph.D. degrees from Waseda university in 1990, 1992, and 1997 respectively. He worked for IBM Tokyo Research Laboratory as a researcher during 1992 to 2005, and then moved to Ochanomizu University. His research interest includes information and scientific visualization, computer graphics, multimedia, and user interface.