Call for Papers: LDAV 2018 – Large Data Analysis and Visualization

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The LDAV 2018 conference on Large Data Analysis and Visualization has issued its Call for Papers. Held in conjunction with IEEE VIS 2018, the event takes place October 21 in Berlin.

The 8th IEEE Large Scale Data Analysis and Visualization (LDAV) symposium 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.

The conference is interested in methods for data at scale, including:

  • 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

The conference is also interested in understanding state of the art techniques,

  • Best practices for large data visualization
  • End-to-end system solutions in a large data context
  • Industry solutions for big data analysis and visualization

The conference is interested in research on the context of 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

Abstracts are due June 17, 2018.

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