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NIST To Hold Data Science Symposium 4-5 March 2014

By Bob Gourley

The National Institute of Standards and Technology (NIST) Information Technology Laboratory is holding a Data Science Symposium we believe will be of high interest to the enterprise CTO community. Symposium topics include the technology landscape of data science, ways to improve analytics, and better ways to approach and manage datasets.

This looks to be a great event to attend if you are interested in helping the community advance.

NIST has issued a call for papers. Please submit your topics in accordance with the below (From: http://ctolink.us/1cPxXGw):

Data Science Symposium 2014


Given the explosion of data production, storage capabilities, communications technologies, computational power, and supporting infrastructure, data science is now recognized as a highly-critical growth area with impact across many sectors including science, government, finance, health care, manufacturing, advertising, retail, and others. Since data science technologies are being leveraged to drive crucial decision making, it is of paramount importance to be able to measure the performance of these technologies and to correctly interpret their output. The NIST Information Technology Laboratory is forming a cross-cutting data science program focused on driving advancements in data science through system benchmarking and rigorous measurement science


The inaugural NIST Data Science Symposium will convene a diverse multi-disciplinary community of stakeholders to promote the design, development, and adoption of novel measurement science in order to foster advances in Big Data processing, analytics, visualization, interaction, and lifecycle management. It is set apart from related symposia by our emphasis on advancing data science technologies through:

  • Benchmarking  of complex data-intensive analytic systems and subcomponents
  • Developing general, extensible performance metrics and measurement methods
  • Creating reference datasets & challenge problems grounded in rigorous measurement science
  • Coordination of open, community-driven evaluations that focus on domains of general interest.

Location: The first symposium, originally scheduled for November, will be held March 4-5, 2014 on the NIST campus in Gaithersburg, MD. Organizing Committee: Ashit Talukder (NIST), John Garofolo (NIST), Mark Przybocki (NIST), Craig Greenberg (NIST) Registration: Registration to attend the NIST Data Science Symposium is now open. Registration is free, but it is necessary to register in order to attend. The deadline for registration will be on or before Friday, February 21st. Registration may close once the capacity of the venue is reached. Please note that only registered participants will be permitted to enter the NIST campus to attend the symposium. To register, please go to: https://www-s.nist.gov/CRS/conf_disclosure.cfm?conf_id=6631 Call For Abstracts: Participants who wish to give presentations of their technical perspectives or present posters (potentially with technical demonstrations) that address symposium topics should submit a brief one-page abstract and brief one-paragraph bio to [email protected] by February 21st, 2014 (those abstracts received after January 10th, 2014 will only be considered for poster presentations). Those who submit abstracts by January 10th will be notified whether their perspectives have been selected for plenary or poster presentation by January 31st. Those submitting abstracts after January 10th and prior to February 21st will be notified whether their perspectives have been selected for a poster presentation on a rolling basis sometime between February 1st and March 1st. Speakers, panelists, and poster presenters will be selected by the organizers based on relevance to symposium objectives and workshop balance. Due to the technical nature of the symposium, no marketing will be permitted. Symposium Topics: Understanding the Data Science Technical Landscape:

  • Primary challenges in and technical approaches to complex workflow components of Big Data systems, including ETL, lifecycle management, analytics, visualization & human-system interaction.
  • Major forms of analytics employed in data science.

Improving Analytic System Performance via Measurement Science

  • Generation of ground truth for large datasets and performance measurement with limited or no ground truth.
  • Methods to measure the performance of data analytic workflows where there are multiple subcomponents, decision points, and human interactions.
  • Methods to measure the flow of uncertainty across complex data analytic systems.
  • Approaches to formally characterizing end-to-end analytic workflows.

Datasets to Enable Rigorous Data Science Research

  • Useful properties for data science reference datasets.
  • Leveraging simulated data in data science research.
  • Efficient approaches to sharing research data.

Travel & Hotel Information: The main NIST campus is located in Gaithersburg, MD approximately 20 miles outside of Washington, DC. Useful travel information, including transportation to NIST as well nearby hotels and restaurants, can be found here: http://www.nist.gov/public_affairs/visitor/index.cfm

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Bob Gourley writes on enterprise IT. He is a founder of Crucial Point and publisher of CTOvision.com

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