活动简介

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.

征稿信息

重要日期

2016-06-17
摘要截稿日期
2016-06-24
初稿截稿日期

征稿范围

  • 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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重要日期
  • 10月23日

    2016

    会议日期

  • 06月17日 2016

    摘要截稿日期

  • 06月24日 2016

    初稿截稿日期

  • 10月23日 2016

    注册截止日期

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