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Data Science Symposium 2014

Purpose:

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.

Organizing Committee:

Ashit Talukder (NIST), John Garofolo (NIST), Mark Przybocki (NIST), Craig Greenberg (NIST)

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 datascience@nist.gov 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.

Agenda:

Revised agenda after weather event >> 

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.

Details:

Start Date: Tuesday, March 4, 2014
End Date: Wednesday, March 5, 2014
Location: NIST campus in Gaithersburg, MD.
Format: Symposium

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

Accommodations:

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.

Hotels:

Several local hotels are listed here: http://www.nist.gov/public_affairs/visitor/hotels.cfm.

Note, both the Hilton and Holiday Inn offer buses to and from NIST. There is not a conference hotel associated with this symposium.

Access to NIST: ·

All symposium registrants will be pre-approved for access to NIST. 2-3 days prior to the event NIST Conference Facilities will send by e-mail conference “dash-passes”. Please print and bring the dash-pass with you on March 4 & 5 for easy entrance to the NIST campus. This forthcoming e-mail will provide everything a visitor needs to know in order to arrive, enter, park, and find their way to the NIST Red Auditorium.

Presentation materials:

Presenters are asked to provide presentation materials and to identify special presenting needs to NIST by 2/21. These materials may be sent directly to datascience@nist.gov. All posters should be designed to be no greater than 45 (width) x 65 (height). Additional guidelines for poster and oral session presenters will be made available shortly.

Please address additional logistic questions to john.roberts@nist.gov.

Technical Contact:

Ashit Talukder
NIST / ITL
Chief, Information Access Division

Craig Greenberg
NIST / ITL

NIST maintains a general mailing list for our Data Science Measurement and Evaluation program. To join this list, please email us using mailto:datascience-list-request@nist.gov?subject=subscribe

Relevant information is posted to this list. If you have any question for NIST related to our data science program, please email us at:

datascience@nist.gov