Call for Papers
Data scales are increasing throughout scientific, business, and research contexts. Large-scale scientific simulations, observation technologies, sensor networks, and experiments are generating enormous datasets, with some projects approaching the multiple exabyte range in the near term.
Gaining insight from massive data is critical for disciplines such as climate science, nuclear physics, security, materials design, transportation, urban planning, and so on. Business-critical decisions are made based on massive data in domains like social media, machine learning, software telemetry, and business intelligence. The tools and approaches needed to search, analyse, and visualize data at extreme scales can be fully realized only from end-to-end solutions, and with collective, interdisciplinary efforts.
The 11th IEEE Large Scale Data Analysis and Visualization (LDAV) symposium, to be held in conjunction with IEEE VIS 2021, is specifically targeting methodological innovation, algorithmic foundations, and possible end-to-end solutions. The LDAV symposium will bring together domain experts, data analysts, visualization researchers, and users to foster common ground for solving both near- and long-term problems.
We are looking for both original research contributions and position papers on a broad-range of topics related to collection, analysis, manipulation, and visualization of large-scale data. We are particularly interested in innovative approaches that combine information visualization, visual analytics, and scientific visualization.
LDAV welcomes papers on techniques and algorithms, systems, application and design studies, empirical studies, state of the practice, and position statements. More descriptions on these paper types can be found here. Representative topics include:
- Distributed, parallel, and multi-threaded computation
- Streaming methods
- Innovative software solutions
- Advanced hardware and GPU-based approaches
- Hierarchical data storage, retrieval, processing, and rendering
- Sampling, approximate query processing, and progressive computation
- Collection, management, and curation of massive datasets
- Scalable visualization and exploration methods
- Ensemble data visualization and analysis
- In-situ data analysis
- Best practices for large data visualization
- End-to-end system solutions in a large data context
- Industry solutions for big data analysis and visualization
- Collaboration or/and co-design of large data analysis with domain experts
- Topics in cognitive issues specific to manipulating and understanding large data
- Application case studies
- New challenges in visualizing experimental, observational, or simulation data
As part of the review criteria, reviewers will be asked to assess whether the contribution is in scope for LDAV, i.e., whether it considers “large data”. Therefore, we strongly encourage you to clearly identify the “large data” aspect you address.
For LDAV, we define large data to be data of size and complexity that require innovation to be processed and understood. With respect to size, the techniques for handling this data require either using atypical hardware or specialized techniques that run on typical hardware. Examples of atypical hardware include supercomputers or novel hardware (such as a just-released GPU, an understudied device like a FPGA, or a high-resolution display), with corresponding techniques including, for example, efficient parallelization. There are many examples of specialized techniques that enable typical hardware to operate effectively on large data; canonical examples including multi-resolution and streaming techniques. With respect to complexity, techniques in scope for LDAV should be illuminating data sets that are considerably larger than typical for a given task, for example, but not restricted to: rendering, layout, analysis, etc. Finally, data may be large relative to the resources available, and such examples are welcomed at LDAV. For example, novel techniques may be needed to visualize or analyze data on a Raspberry Pi or sensor network.
LDAV is accepting both full papers and short papers. The manuscripts should be formatted according to guidelines from IEEE VGTC. Submission of an abstract is required prior to submission of a paper or short paper.
Submission site note: Go to the submission site (https://new.precisionconference.com/vgtc), log in, go to ‘Submissions’, and select Society ‘VGTC’, Conference ‘LDAV 2021’, and Track ‘LDAV 2021 Papers’.
Full papers should have a maximum length of 9 pages with up to two (2) additional pages allowed for only references (maximum total of 11 pages). Full papers may make contributions in techniques, systems, applications, evaluations, or theory. The contributions of full papers are reviewed based on their novelty, contribution, replicability, and evaluation.
Short papers should have a length of 4-5 pages in total. Short papers are a venue to report smaller contributions than full papers. Position papers and showcases of interesting application of visualization are good topics for short papers. Technique, system, application, evaluation, or theory papers that have a smaller contribution than a full paper can also be submitted as a short paper.
The proceedings of the symposium will be published together with the VIS proceedings and via the IEEE Xplore Digital Library.
The LDAV Program Committee will present a Best Paper award to the authors whose submission is deemed the strongest according to the reviewing criteria. This award will be announced in conjunction with VIS 2021.
The Best Paper for LDAV will be published directly in IEEE Transactions on Visualization and Computer Graphics (TVCG). Further, in accordance with TVCG’s policy for extending conference papers, some excellent papers will be encouraged to submit journal versions of their work to TVCG, with reviewer continuity.
Please note: all deadlines are firm and no extensions will be granted.
- Abstract Deadline (firm)
- June 21, 2021, 11:59 PM (AOE)
- Paper Submission (firm)
- June 25, 2021, 11:59 PM (AOE)
- Author Notification
- July 30, 2021
- Camera-Ready Deadline (firm)
- August 15, 2021