Division of Information and Intelligent Systems
IIS: Information Integration and Informatics (III)
|Wei Dingemail@example.com||(703) 292-8930|
|Wei-Shinn Kufirstname.lastname@example.org||(703) 292-8318|
|Wendy Nilsenemail@example.com||(703) 292-2568|
|Amarda Shehufirstname.lastname@example.org||(703) 292-7842|
|Sylvia J. Spengleremail@example.com||(703) 292-8930|
|Maria Zemankovafirstname.lastname@example.org||(703) 292-7348|
The III program supports innovative research on computational methods for the full data lifecycle, from collection through archiving and knowledge discovery, to maximize the utility of information resources to science and engineering and broadly to society. III projects range from formal theoretical research to those that advance data-intensive applications of scientific, engineering or societal importance. Research areas within III include:
- General methods for data acquisition, exploration, analysis and explanation: Innovative methods for collecting and analyzing data as part of a scalable computational system.
- Domain-specific methods for data acquisition, exploration, analysis and explanation: Work that advances III research while leveraging properties of specific application domains, such as health, education, science or work. Note that projects that simply apply existing III techniques to particular domains of science and engineering are more appropriate for funding opportunities issued by the NSF directorates cognizant for those domains.
- Advanced analytics: Novel machine learning, data mining, and prediction methods applicable to large, high-velocity, complex, and/or heterogenous datasets. This area includes data visualization, search, information filtering, knowledge extraction and recommender systems.
- Data management: Research on databases, data processing algorithms and novel information architectures. This topic includes representations for scalable handling of various types of data, such as images, matrices or graphs; methods for integrating heterogenous and distributed data; probabilistic databases and other approaches to handling uncertainty in data; ways to ensure data privacy, security and provenance; and novel methods for data archiving.
- Knowledge bases: Includes ontology construction, knowledge sharing, methods for handling inconsistent knowledge bases and methods for constructing open knowledge networks through expert knowledge acquisition, crowdsourcing, machine learning or a combination of techniques.