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09 May 2021
Big Data Visualization
High volumes of data are being generated at an ever-increasing rate from a wide variety of sources such as health care, manufacturing, IoT, cybersecurity, financial sectors, social media to name a few. Data is not only voluminous but also dynamic, noisy, heterogeneous, and often completely unstructured. It is no longer possible to analyze the raw data merely through traditional methods. More than ever, there is a strong need for effective visualizations of big data as an integral part of the analysis process. There are key research challenges in big data visualization both from the perspective of how we can build tools and systems that facilitate responsive visual data analysis regardless of the size and nature of the data, as well as how to leverage prolific and complex data to improve understanding via visualization, interactive data visualization in particular.
Big data visualization forces a much greater emphasis on developing effective ways to navigate large data to arrive at insights and brings with it logistical challenges relating to infrastructures such as storage, networking, and computational capacities. Modern big data visualization relies on a cross-section of disciplines including statistics, human factors, computer science, design, and network engineering. Building effective data science tools for analyzing, exploring, and drawing insights from big data visualization is rapidly becoming the cornerstone of modern science across the board.
The objective of this special issue is to highlight cutting-edge, transdisciplinary work to improve big data visualization capabilities, with a particular emphasis on interactive data visualization of large, complex data.
Topics for the Special Issue
Topics of interest for the special issue include, but are not limited to:
- Novel sampling techniques
- Progressive data analysis
- Novel Filtering and aggregation techniques
- Novel precomputation techniques
- Parallelizing data fetching.
- Efficient predictive caching techniques
- Alternate storage schemes and data structures for efficient data management
- Novel Interface
- Novel query language
- Machine learning methods for improving big data visualization
- Improvements in scientific visualization
- Software and hardware infrastructure to improve big data visualization
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All submissions will be peer-reviewed and judged on correctness, originality, technical strength, significance, quality of presentation, and relevance to the special issue topics of interest. Submitted papers may not be under consideration for another conference or journal, nor may they be under review or submitted to another forum during the review process. The submissions should be prepared according to the guidelines of IJHCI. These can be found at the following link: https://www.tandfonline.com/action/authorSubmission?show=instructions&journalCode=hihc20
All submissions should be done through the editorial manager of IJHCI: https://www.editorialmanager.com/ijhc/default.aspx
- Submission Deadline: May 9th, 2021
- Completion of First Round of Review: July 3rd, 2021
- Revised Manuscript Due: September 4th, 2021
- Notification of Final Decision: October 2nd, 2021
- Final Manuscript Due: October 30th, 2021
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