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.
The first symposium has been postponed and will now be held March 4-5, 2014 on the NIST campus in Gaithersburg, MD.
Ashit Talukder (NIST), John Garofolo (NIST), Mark Przybocki (NIST), Craig Greenberg (NIST)
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, January 10. 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:
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 firstname.lastname@example.org by January 10th, 2014. Submitters will be notified whether their perspectives have been selected for plenary or poster presentation by January 31st. 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.
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.