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LDAV : IEE 2013 - IEEE Symposium on Large-Scale Data Analysis and Visualization 2013

Date2013-10-13 - 2013-10-14


VenueGeorgia, USA - United States USA - United States



Topics/Call fo Papers

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 them into insight is critical for a variety of disciplines including climate science, nuclear physics, security, materials design, transportation, and urban planning. This is currently referred to as the Big Data Challenge. The tools and approaches needed to mine, analyze, and visualize data at extreme scales can be fully realized only if we have end-to-end solutions, which demands collective, interdisciplinary efforts.
The Large Scale Data Analysis and Visualization (LDAV) symposium, to be held in conjunction with IEEE VisWeek 2013, is specifically targeting possible end-to-end solutions. The LDAV symposium will bring together domain scientists, data analysts, visualization researchers, users, designers and artists, to foster common ground for solving both near- and long-term problems.
We are looking for original research contributions on a broad-range of topics related to the 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:
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
Data mining and machine learning techniques for large data analysis
Advanced hardware and system architectures for data handling, analysis or visualization
Hierarchical data storage, retrieval or rendering
Distributed, parallel or multi-threaded approaches
MapReduce-based and Database-related methods, algorithms or approaches
Data collection, management and curation
Collaboration or co-design of data analysis with domain scientists
Application case studies
Topics in cognitive issues specific to manipulating and understanding large data
Industry solutions for Big Data analytics and infrastructure

Last modified: 2013-03-17 17:13:24