[Infovis] CFP: IEEE VIS Workshop (Vis + Prov) x Domain

[Kai Xu]

IEEE VIS Workshop (Vis + Prov) x Domain
visxprov at googlegroups.com<mailto:visxprov at googlegroups.com>

We invite submissions for the IEEE VIS Workshop (Vis + Prov) x Domain: Workshop on Visualization and Provenance Across Domains (https://visxprov.github.io/).

Workshop Goals: Our ambition is to build this into a series of workshops, targeting a different research community outside visualization each year, and eventually create a provenance research network that connects all the relevant communities.

For this year, we will target the database community, which is one of the first communities to formalize provenance research, providing the theoretical foundations such as the widely adopted W3C PROV specifications. Recently provenance and visualization have been adopted to address core database challenges such as query, workflow, and dependency. There is a clear need to build closer connections between the two communities.

For this year, the workshop’s main goals are:


  1.  To establish and improve the collaboration between the visualization and database community on provenance-related research through a discussion of challenges that span both communities;
  2.  To further advance data provenance research within the database community to address the latest challenges, such as support for big data management, data processing, data sharing, or query optimization supported by provenance;
  3.  To further advance analytic provenance research within the visualization community to address the latest challenges, such as support for machine learning and visualization reproducibility.

Scope of Topics: While we are interested in any submissions at the intersection of provenance and visualization, we are especially focused on:


  *   What can we learn when contrasting the problems of these two communities? Is there any visualization problem that can benefit from a database/analytic provenance method or vice versa?
  *   What are the most pressing provenance-related challenges in the database community? Is there any visualization-related solution to them? For example,
     *   What visualization techniques may be used to assist the database community in constructing retrospective and prospective provenance that are more interactive, scalable, and user-friendly?
     *   How can query languages be supported from the standpoint of provenance?
  *   What are the opportunities for provenance in emerging visualization research areas such as machine learning visualization and visualization reproducibility? For example,
     *   How do we show changes to visualizations, interactions, or algorithms over time in various domains, including progressive visualizations, interactive visualizations, and machine learning training?
     *   How do we transparently and efficiently share visualizations and their production processes?

Submission: We will accept research papers/extended abstracts or position papers. Your submission should be commensurate with the level of contribution but is required to be at least 2 pages (plus references). Papers must follow the IEEE VIS TVCG Journal submissions guidelines (https://tc.computer.org/vgtc/publications/journal/) and be submitted through the Precision Conference System (PCS) (https://new.precisionconference.com/vgtc). Your submission should be anonymized to facilitate a double-blind review between authors and the conference organizers. Accepted authors will be invited to post their work in an arXiv collection (which will not be considered archival). We are in contact with journals about the possibility of a special issue for the work accepted by this workshop.

Important Dates:

July 15, 2023: Paper submission deadline
August 3, 2023: Paper author notification
August 13, 2023: Paper camera-ready version deadline
Notice: all times are midnight Anywhere on Earth (AOE)

We hope to see you in Melbourne, Australia!

Kai Xu, University of Nottingham
Michelle Dowling, Pacific Northwest National Laboratory
John Wenskovitch, Pacific Northwest National Laboratory
Jeremy E. Block, University of Florida
Yilin Xia, University of Illinois Urbana-Champaign



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