Modern large-scale scientific simulations, sensor networks, and experiments are generating enormous datasets, with some projects approaching the multiple exabyte range in the near term. Managing and analyzing large datasets in order to transform it into insight is critical for a variety of disciplines including climate science, nuclear physics, security, materials design, transportation, and urban planning. The tools and approaches needed to mine, analyze, and visualize data at extreme scales can be fully realized only if there are end-to-end solutions, which demands collective, interdisciplinary efforts.

The 7th IEEE Large Scale Data Analysis and Visualization (LDAV) symposium, to be held in conjunction with IEEE VIS 2017, is specifically targeting methodological innovation, algorithmic foundations, and possible end-to-end solutions. The LDAV symposium will bring together domain scientists, data analysts, visualization researchers, and users to foster common ground for solving both near- and long-term problems.

Scope

We are looking for original research contributions on a broad-range of topics related to collection, analysis, manipulation or visualization of large-scale data. We also welcome position papers on these topics.

Topics of interest include, but are not limited to:

• Streaming methods for analysis, collection and visualization
• Advanced hardware for data handling or visualization
• Innovative software solutions and best practices for large data visualization
• Distributed, parallel or multi-threaded approaches
• Spark-based, MapReduce-based and database-related methods, algorithms or approaches
• End-to-end system solutions in a large data context
• Hierarchical data storage, retrieval or rendering
• In situ visualization techniques
• Collaboration or co-design of large data analysis with domain scientists
• Topics in cognitive issues specific to manipulating and understanding large data
• Application case studies
• Industry solutions for big data
• Innovative approaches combining information visualization, visual analytics, and scientific visualization
• Novel methods for understanding and interacting with extreme-scale data
• New challenges in visualizing experimental, observational, or simulation data
• Collection, management and curation of massive datasets