NIST Data Science Symposium 2013

"Given the explosion of data production, storage capabilities, communications technologies, computational power, and supporting infrastructure, data science is now recognized across all sectors as a highly-critical growth area for information technology. NIST and the Information Technology Laboratory (ITL) at NIST has over 25 years of experience measuring and evaluating technologies used to process, analyze, and derive knowledge from a multitude of structured and unstructured data. ITL is forming a cross-cutting data science program focused on driving advancements in big data analytics through a new benchmarking and evaluation series and by developing a model of data science that will identify the various attributes that can be measured, e.g., component classes, component-level performance, end-to-end performance, flow analyses, and propagation of uncertainty. This series will focus research community critical mass on the hard problems in data science by supporting objective comparison of approaches, identifying key areas for improvement in data analytics, and defining challenge problems grounded in rigorous measurement science. The inaugural NIST Data Science Symposium is planned for November 18-19, 2013, and will be set apart from related symposia by our emphasis on advancing the technology through the development of metrics and measurement methods, the creation of reference datasets, and the coordination of open community-wide evaluations that focus on domains and use-cases of general interest ... The 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, and interaction, via:  [1] Developing metrics and methods appropriate for measuring the performance of scalable approaches to massive data analytics, visualization, & processing [2] Creating reference datasets for domain-specific and domain agnostic benchmarking and evaluation [3] Posing challenge problems with rigorous metrics for technically compelling data science problems [4] Novel methods for ground truth generation and calibration on massive scales of data [5] Facilitating creation & sharing of common platforms, datasets, algorithms & tools to advance the state of the art [6] Utilizing big data to enhance solutions for cybersecurity, networked systems, and other domains."