In many areas of science, simulations and experiments begin to generate many petabytes of data, with some sciences facing exabytes of data near term. Similarly, the collection of information about the Internet applications and users for a variety of purposes is generating only more data. Our ability to manage, mine, analyze, and visualize the data is fundamental to the knowledge discovery process. That is, the value of data at extreme scale can be fully realized only if we have an end-to-end solution, which demands a collective, inter-disciplinary effort to develop.
This symposium, held in conjunction with IEEE VIS 2016, aims at bringing together domain scientists, data analytics and visualization researchers, and users, and fostering the needed exchange to develop the next-generation data-intensive analysis and visualization technology. Attendees will be introduced to the latest and greatest research innovations in large data management, analysis, and visualization, learn how these innovations impact data intensive computing and knowledge discovery, and also learn about the critical issues in creating a complete solution through both invited and contributed talks, and panel discussion. Paper submissions are solicited for a long paper event that describes large data visualization techniques and systems, and a short paper event for practitioners to describe and present their large data visualization applications. Topic emphasis is on algorithms, languages, systems and hardware that supports the analysis and visualization of large data.
There are a variety of ways to participate in LDAV 2016 - papers, posters, and attending. We hope to see you there! Registration is through the IEEE VIS website.
Data collection, management and curation
Innovative approaches combining information visualization, visual analytics, and scientific visualization
Streaming methods for analysis, collection and visualization
Novel, extreme or innovative methods for understanding and interacting with data
Advanced hardware for data handling or visualization
Distributed, parallel or multi-threaded approaches
MapReduce-based and database-related methods, algorithms or approaches
Hierarchical data storage, retrieval or rendering
Collaboration or co-design of data analysis with domain scientists
Topics in cognitive issues specific to manipulating and understanding large data
Application case studies
Industry solutions for big data
End-to-end system solutions
New challenges in visualizing experimental, observational, or simulation data
In situ visualization techniques
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