Announcement
Critical Techniques, Technologies and Methodologies for Advancing Foundations & Applications of Big Data Sciences and Eng.

PROGRAM SOLICITATION: CRITICAL TECHNIQUES, TECHNOLOGIES AND METHODOLOGIES FOR ADVANCING FOUNDATIONS AND APPLICATIONS OF BIG DATA SCIENCES AND ENGINEERING (BIGDATA) - NSF 17-534

View the full solicitation at: https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504767

SYNOPSIS

The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science. 

The solicitation invites two categories of proposals:

Foundations (F): those developing or studying fundamental theories, techniques, methodologies, and technologies of broad applicability to big data problems, motivated by specific data challenges and requirements; and
Innovative Applications (IA): those engaged in translational activities that employ new big data techniques, methodologies, and technologies to address and solve problems in specific application domains. Projects in this category must be collaborative, involving researchers from domain disciplines and one or more methodological disciplines, e.g., computer science, statistics, mathematics, simulation and modeling, etc.

Proposals in both categories must include a clear description of the big data aspect(s) that have motivated the proposed approach(es), for example: the scalability of methods with increasing data volumes, rates, heterogeneity; or data quality and data bias; etc. Innovative Applications proposals must provide clear examples of the impacts of the big data techniques, technologies and/or methodologies on (a) specific domain application(s).

Proposals in all areas of sciences and engineering covered by participating NSF directorates and partnering agencies [the Office of Financial Research (OFR)], are welcome.

Before preparing a proposal in response to this BIGDATA solicitation, applicants are strongly urged to review other related programs and solicitations and contact the respective NSF program officers listed in them should those solicitations be more appropriate. In particular:

For the development of robust and shared data-centric cyberinfrastructure capabilities, applicants should consider the Data Infrastructure Building Blocks (DIBBs) program, https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504776;
For computational and data science research not specifically addressing big data issues, applicants should consider the Computational and Data Enabled Science and Engineering (CDS&E) program, http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504813;
For work that is focused more on scaling of software, rather than data-related issues, applicants should consider the Scalable Parallelism in the Extreme (SPX) program, https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505348;
Proposals that are specific to the geosciences, and respond to the community needs and requirements expressed by the geosciences community, should consider the NSF EarthCube program for Developing a Community-Driven Data and Knowledge Environment for the Geosciences, https://www.nsf.gov/geo/earthcube/;
Proposals that focus on research in mathematics or statistics that is not tied to a specific big data problem should be submitted to the appropriate program within the MPS Division of Mathematical Sciences (DMS); see a list of DMS programs at https://www.nsf.gov/funding/programs.jsp?org=DMS; and
Proposals that focus on research in the computer and information sciences not tied to a specific big data problem should be submitted to the appropriate CISE core program:

Computer and Network Systems (CNS) Core Programs: https://nsf.gov/publications/pub_summ.jsp?WT.z_pims_id=12765&ods_key=nsf16579;

Computing and Communication Foundations (CCF) Core Programs: https://nsf.gov/publications/pub_summ.jsp?WT.z_pims_id=503220&ods_key=nsf16578; and
Information and Intelligent Systems (IIS) Core Programs: https://nsf.gov/publications/pub_summ.jsp?WT.z_pims_id=13707&ods_key=nsf16581.

IMPORTANT INFORMATION FOR PROPOSERS

A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 17-1), is effective for proposals submitted, or due, on or after January 30, 2017. Please be advised that, depending on the specified due date, the guidelines contained in NSF 17-1 may apply to proposals submitted in response to this funding opportunity.

Amazon Web Services (AWS), Google, and Microsoft are now participating in the solicitation by providing cloud credits/resources to qualifying projects. The solicitation provides details regarding the participation of these companies, and the use of their cloud resources.

Additionally, slight revisions of the Program Description have been introduced.

Any proposal submitted in response to this solicitation should be submitted in accordance with the revised NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 17-1), which is effective for proposals submitted, or due, on or after January 30, 2017.

AWARD INFORMATION

Anticipated Type of Award: Standard Grant or Continuing Grant or Cooperative Agreement

Estimated Number of Awards: 27 to 35

About 27-35 projects will be funded, subject to availability of funds.

Anticipated Funding Amount: $26,500,000

Up to $26,500,000 will be invested by NSF and the Office of Financial Research (OFR), in proposals submitted to this solicitation, subject to the availability of funds. Up to $9,000,000 will be invested by Amazon Web Services (AWS), Google, and Microsoft (up to $3,000,000 each) in the form of cloud credits/resources.

Projects will typically receive NSF funding in the range of $200,000 to a maximum of $500,000 per year, for 3 to 4 years of support. The minimum award size will be $600,000 of total NSF/OFR funding, reflecting the minimum expected level of effort for BIGDATA projects, which are expected to be multidisciplinary in nature and include significant student involvement. Any allocation of cloud credits/resources from AWS, Google or Microsoft will be in addition to the NSF/OFR funding.

Submission Window Date(s): March 15, 2017 - March 22, 2017

General Announcement
Not in Slideshow
Katie Dey Submitted by Katie Dey on June 30th, 2017
Project
CPS: Synergy: Collaborative Research: Real-time Data Analytics for Energy Cyber-Physical Systems
Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area. The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.
1660025
Off
New Jersey Institute of Technology
Maggie Cheng
-
National Science Foundation
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660025&HistoricalAwards=false
Submitted by Maggie Cheng on June 19th, 2017
Event
IoTBDS 2017
2nd International Conference on IoT, Big Data and Security (IoTBDS 2017) 24-26th April 2017 | Porto, Portugal | http://iotbds.org/ Sponsored by INSTICC - Institute for Systems and Technologies of Information, Control and Communication
Submitted by Anonymous on December 15th, 2016
Project
RCN: SAVI: Adaptive Management and Use of Resilient Infrastructures in Smart Cities: Support for Global Collaborative Research on Real-Time Analytics of Heterogeneous Big Data
Cities provide ready and efficient access to facilities and amenities through shared civil infrastructures such as transportation and healthcare. Making such critical infrastructures resilient to sudden changes, e.g., caused by large-scale disasters, requires careful management of limited and varying resources. The rapidly growing big data from both physical sensors and social media in real-time suggest an unprecedented opportunity for information technology to enable increasing efficiency and effectiveness of adaptive resource management techniques in response to sharp changes in supply and/or demand on critical infrastructures. Within the general areas of resilient infrastructures and big data, this project will focus on the integration of heterogeneous Big Data and real-time analytics that will improve the adaptive management of resources when critical infrastructures are under stress. The integration of heterogeneous data sources is essential because many kinds of physical sensors and social media provide useful information on various critical infrastructures, particularly when they are under stress. This Research Coordination Network (RCN) will promote meetings and activities that stimulate and enable new research on integration of heterogeneous physical sensor data and social media for real-time big data analytics in support of resilient critical infrastructures such as transportation and healthcare in smart cities. As first example, the RCN will support participation from young faculty attending the Early Career Investigators' Workshop on Cyber-Physical Systems in Smart Cities (ECI-CPS) at CPSweek (April of each year) and young faculty attending the Workshop on Big Data Analytics for Cyber-physical Systems (BDACPS). As a second example, the RCN will support contributions to a Special Track on Big Data Analytics for Resilient Infrastructures at the IEEE Big Data Congress. As a third example, the RCN will support participation in International meetings organized by other countries, e.g., Japan's Big Data program by Japan Science and Technology Agency (JST). The project will also maintain a repository of research resources. Concretely, the RCN will actively collect and make readily available public data sets (e.g., physical and social sensor data) and software tools (e.g., to support real-time big data analytics). The technologies and tools that arise from RCN-enabled research will be applied to socially and economically impactful areas such as reducing congestion and personalized healthcare in smart cities.
1550379
Off
Georgia Institute of Technology
Calton Pu
-
National Science Foundation
http://www.nsf.gov/awardsearch/showAward?AWD_ID=1550379&HistoricalAwards=false
Submitted by Calton Pu on April 11th, 2016
Project
CPS-EAGER- Experiments with Smart City Hubs: Integration Platform for Human Cyber-Physical Systems In Smart Cities
The objective of this research is to understand the complexities associated with integration between humans and cyber-physical systems (CPS) at large scales. For this purpose, the team will develop and demonstrate the application of Smart City Hubs focusing on intelligent transportation services in urban settings. Ultimately, this project will produce innovative tools and techniques to configure and deploy large-scale scale experiments enabling the study of how humans affect the control loops in large CPS such as smart cities. This work covers several design concerns that are specific to human-CPS such as human computer interfaces, decision support systems and incentives engineering to keep humans engaged with the system. The technology base will include a novel integration platform for allowing (1) integration of spatially and temporally distributed sensor streams; (2) integration of simulation-based decision support systems, (3) development and execution of experiments to understand how advanced decision support tools combined with incentive mechanisms improve the utilization of the transportation infrastructure and user experience. A key aspect of this research will be development of data-driven rider models that can be subsequently used by city engineers for planning purposes. The proposed system will enable a new generation of human-CPS systems where sensing, wireless communication, and data-driven predictive analytics is combined with human decision-making and human-driven actuation (driving and physical infrastructure utilization) to form a control loop. The Smart City Hub provides a generic platform for a number of other services beyond traffic and public transportation, including maps and way finding, municipal communication, emergency management and others. The tools that will be developed will allow researchers and practitioners to more quickly prototype, deploy and experiment with these CPS. To ensure these benefits, the research team will make its research infrastructure freely available as an open-source project. It will also develop educational materials focused on modeling, prototyping and evaluating these applications at scale. In addition, the studies the team will perform will provide new data and new scientific understanding of large-scale human interaction with CPS, which it expects will yield long-term benefits in the design and analysis of such applications.
1528799
Off
Vanderbilt University
Abhishek Dubey
-
National Science Foundation
Abhishek Dubey Submitted by Abhishek Dubey on December 22nd, 2015
Event
I-SPAN 2014
The 13th International Symposium on Pervasive Systems, Algorithms, and Networks (I-SPAN 2014) http://umc.uestc.edu.cn/conference/ISPAN2014 Dec. 19th - 21st, 2014, Chengdu, Sichuan, China I-SPAN 2014 is to bring together computer scientists, industrial engineers, and researchers to discuss and exchange experimental and theoretical results, novel designs, work-in-progress, experience, case studies, and trend-setting ideas in the areas of parallel architectures, algorithms, networks, and internet technology.
Submitted by Anonymous on May 19th, 2014
Event
IEEE Cloud-CPS 2014
IEEE International Workshop on Cloud-integrated Cyber Physical Systems 2014 (IEEE Cloud-CPS 2014) in conjunction with IEEE CloudCom 2014, December 15 - 18, 2014, Singapore   Overview:
Submitted by Anonymous on May 19th, 2014
Subscribe to Big Data