The White House announced in 2012 the Big Data Initiative to mobilize the research and development enterprise towards Big Data analytics for solving some of the nation's most pressing challenges. Signal processing and systems engineering communities can be important contributors to Big Data research and development, complementing computer and information science-based efforts in this direction. Recognizing the need to engage these communities, this workshop aims to bring together experts from various fields to carve out the role that analytical and experimental engineering has to play in Big Data research and development. Such a workshop will be unique in emphasizing signal analytics and systems engineering aspects of Big Data. Time is of essence, as the premise is to engage the engineering community and help shape up research directions for the next few years.
Intellectual Merit: The workshop will address key questions in big data analytics in engineering disciplines. Topics of interest include multidimensional signal analytics that build upon a signals and systems fabric, and systems engineering is sure to play an important role in designing and deploying such large, distributed, fault-tolerant systems.
Broader Impacts: This workshop will help engage engineering disciplines in Big Data research and development. The discussion results from the workshop will have significant impact on the research and development for big-scale engineering projects in the data-driven information age, as well as the training and education for future engineers. The workshop report will provide a useful resource for the scientific community and general public.
On March 29, 2012, the White House announced the Big Data Research and Development Initiative to mobilize the research and development community towards Big Data analytics for solving some of the nation’s most pressing challenges. A year later, NSF’s Engineering Directorate sponsored this workshop on signals and systems aspects of Big Data, under stewardship of the Electrical Communications and Cyber Systems (ECCS) program. The objectives of the workshop were to i) Gather and synthesize engineering perspectives on grand challenges in Big Data, and how engineers can contribute to Big Data systems research; ii) Discuss the role that NSF’s Engineering Directorate should play in Big Data research; and iii) Discuss ways to educate engineers about Big Data. The workshop brought together some of the most prominent researchers on the subject matter in the relevant disciplines, with a total of 85 participants from academia, industry, and government agencies (NSF, ARL, AFOSR, NRL, and DARPA). Intellectual merit: The workshop helped outline what is truly unique and challenging in modern massive datasets in general, and big engineering data in particular - which tend to be more tightly structured. The research directions and recommendations that have emerged from the workshop deliberations were distilled in three research thrusts: Statistical Signal and Systems Theory and Optimization; Hardware-Software and Analog-Digital Hybrid Systems for Big Data; and Big Data infrastructure: Cloud Storage, Distributed Coding, and High-Performance Computing. For each of these thrusts, important and timely research directions were outlined to assist NSF and the engineering research community to better navigate Big Data challenges in engineering and beyond. Broader impacts: Big data is a multi-disciplinary area that draws upon data mining, machine learning, signal processing, statistics, applied mathematics, and computer engineering. This workhop has addressed one of the constituent communities, with the aim of raising awareness and interest in this exciting cross-disciplinary research area. Faculty, graduate students, researchers from industry, and program managers from the major government agencies have attended talks and panel discussions with leading experts on some of the latest topics in Big Data research. .
