Our society has entered a data-driven era, in which not only are enormous amounts of data being generated every day, but also growing expectations are placed on their analysis. Even today, analyzing these massive and complex datasets remains a difficult task. Network data, in particular, is a type of data that has become omnipresent. It is intrinsically relational data between entities, with or without a spatial mapping. Because of this relational nature, network data can be found in a great diversity of areas: communication networks, power grid networks, air traffic networks, biological networks and many more. Understanding and taking advantage of this data is very important in order to improve our knowledge in the various domains. However, analysing network data is very challenging, especially when networks are dense and complex.

While new knowledge extraction algorithms based on automatic systems or Machine Learning are giving promising results (i.e. deep learning), they suffer from a lack of flexibility and from the black box effect, where the result of the analysis is given without any explanation. Visual Analytics offers a user-centric approach which places human analysts at the center of the interpretation and decision process. Visual Analytics, leverages the user’s visual system and cognitive abilities to understand potentially large and complex data [6, 10].

Visual Analytics has recently gained interest with the rapid development and commodification of virtual reality head-mounted display devices which have been largely motivated by obvious opportunities in entertainment. Devices like Microsoft HoloLens represent a similar stepchange in the adoption of Augmented Reality in straightforward applications such as situated architectural walk-through and useful overlays of engineering models on their real-world counterparts. These initial explorations represent applications that are impossible with traditional desktop computing environments. However, as these types of devices become ubiquitous, researchers are starting to explore how more traditional computing applications such as data analysis will be conducted in Virtual and Augmented Reality (VR/AR). A topic of study is emerging from the Information Visualisation (InfoVis) and AR/VR research communities exploring how data analysis can be reimagined with—and benefit from—such emerging display and interaction technologies: Immersive Analytics [2]. Despite initial studies exploring the potential of these emerging technologies for general data visualisation, there is little understanding yet about how best to accomplish immersive network visualisation. Initial studies showed that this is a very promising area for immersive analytics research [1, 3, 4, 7, 8, 9, 11].

This seminar is at the crossroads of data visualisation, computer graphics and interactive data analysis for decision making. The research community has today the opportunity to take advantage of the recent technological improvements to forecast their future usage, and this seminar aims to discuss and develop technical approaches, design guidelines, for achieving effective immersive visual analysis.

During this seminar, we will foster discussions with researchers and practitioners around emerging technology to support decision making in immersive environments. The proposed seminar represents a straight continuation of the previous Shonan and Dagstuhl meetings:

• Dynamic Networks Visual Analytics (Shonan, 2015)
  (http://shonan.nii.ac.jp/shonan/blog/2015/10/22/dynamic-networks-visual-analytics-approaches-to-facilitate-visual-analysis-of-complex-and-dynamic-network-data/ )
• Immersive Analytics:A new multidisciplinary initiative to explore future interaction technologies for data analytics (Shonan, 2016)
  (http://shonan.nii.ac.jp/shonan/blog/2015/04/01/immersive-analytics-a-new-multidisciplinary-initiative-to-explore-future-interaction-technologies-for-data-analytics/)
  
• Immersive Analytics (Dagstuhl, 2016)
  (https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16231 )

This new meeting will focus on emerging technologies and recent scientific advance to support decision making with network data and will investigate topics such as Information Visualization (InfoVis), Graph Drawing, Collaboration, Immersive and mixed reality technologies, interaction techniques, data storytelling, scalability issues. During this meeting we will target our discussion on a few specific application domains: Air Traffic Control, Brain activity network, social network, biological networks and broader traffic flows. During this seminar, participant will reflect upon the following research questions:

1- Future technologies: what are the recent and future technological improvements which will foster network data analytics activity in immersive environment (display, interaction devices)?
2- Maintaining the user in the decision loop: How interactive and immersive environment can seamlessly intervene with user in an ecological decision making environment with relational data?
3- Data faithfulness and efficient data representation: What are the current and the future efficient network data visualizations and interaction paradigms that best fit visual analytics requirements?
4- How to foster collaboration: Immersive Analytics triggers many user collaboration limitations and some of them can be addressed thanks to specific design guidelines. Can one envisage future technological improvements to support efficient user collaboration in immersive
environments with relational data and their network representations?

Our goal is to go beyond these discussions and the interactions enabled by this seminar and to make them concrete and sharable with the research community. We have reached an agreement with the Journal of Visual Informatics ( https://www.journals.elsevier.com/visual-informatics/ ) to publish peer reviewed papers in a special issue of the Joural on immersive visual analytics. The paper selection will operate as follows. During this seminar, relevant topics will be defined by the participants and groups will be organised around these topic areas. Each group will work on its topic and report to others their progress at the end of each day. At the end of the seminar, topics and a potential paper structure will be discussed by the participants. After the seminar, an open call for the special issue will be announced. Everyone can submit a paper but the seminar will ensure that the core relevant topics are covered. Each paper will be peer reviewed to assure their alignment with the topic and their scientific quality. Finally, high quality papers will be selected and published.

[1] Billen, M. I., Kreylos, O., Hamann, B., Jadamec, M. A., Kellogg, L. H., Staadt, O., and Sumner, D. Y. 2008. A geoscience perspective on immersive 3D gridded data visualization. Computers & Geosciences 34, 9, 1056–1072.
[2] Chandler, T., Cordeil, M., Czauderna, T., Dwyer, T., Glowacki, J., Goncu, C., Klapperstueck, M., Klein, K., Marriott, K., Schreiber, F., et al. Immersive analytics. In Big Data Visual Analytics (BDVA), 2015, IEEE (2015), 1–8.
[3] Ciro Donalek, S George Djorgovski, Alex Cioc, Anwell Wang, Jerry Zhang, Elizabeth Lawler, Stacy Yeh, Ashish Mahabal, Matthew Graham, Andrew Drake, and others. 2014. Immersive and collaborative data visualization using virtual reality platforms. In Big Data (Big Data), 2014 IEEE International Conference on. IEEE, 609–614.
[4] Cordeil, Maxime, et al. “Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display?.” IEEE Transactions on Visualization and Computer Graphics 23.1(2017): 441-450.
[5] Donalek, C., Djorgovski, S. G., Davidoff, S., Cioc, A., Wang, A., Longo, G., Norris, J. S., Zhang, J., Lawler, E., Yeh, S., Mahabal, A., Graham, M., and Drake, A. 2014. Immersive and Collaborative Data Visualization Using Virtual Reality Platforms.
[6] Keim, Daniel, et al. “Visual analytics: Definition, process, and challenges.” Information visualization. Springer Berlin Heidelberg, 2008. 154-175.
[7] Mark Billinghurst and Hirokazu Kato. 1999. Collaborative Mixed Reality. Proc. of ISMR ’99, Springer, 261–284.
[8] O. Kwon, C. Muelder, K. Lee, and K. Ma. 2016. A Study of Layout, Rendering, and Interaction Methods for Immersive Graph Visualization. IEEE TVCG 22, 7 (2016), 1802–1815.
[9] Slater M. & Wilbur S. A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence: Teleoperators and virtual environments 6, 6 (1997).
[10] Thomas, J. J. and Cook, K. A. 2006. A visual analytics agenda. IEEE Comput. Grap. Appl. 26, [11] Ware, Colin, and Peter Mitchell. “Visualizing graphs in three dimensions.” ACM Transactions on Applied Perception (TAP) 5.1 (2008): 2.