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Research Spending & Results

Award Detail

Doing Business As Name:University of Florida
  • My T Thai
  • (352) 328-3000
Award Date:09/13/2019
Estimated Total Award Amount: $ 250,000
Funds Obligated to Date: $ 250,000
  • FY 2019=$250,000
Start Date:10/15/2019
End Date:09/30/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:III: Small: Collaborative Research: Stream-Based Active Mining at Scale: Non-Linear Non-Submodular Maximization
Federal Award ID Number:1908594
DUNS ID:969663814
Parent DUNS ID:159621697
Program:Info Integration & Informatics
Program Officer:
  • Wei Ding
  • (703) 292-8017

Awardee Location

Awardee Cong. District:03

Primary Place of Performance

Organization Name:University of Florida
Street:University of Florida
City:Gainesville, FL
Cong. District:03

Abstract at Time of Award

The past decades have witnessed enormous transformations of intelligent data analysis in the realm of datasets at an unprecedented scale. Analysis of big data is computationally demanding, resource hungry, and much more complex. With recent emerging applications, most of the studied objective functions have been shown to be non-submodular or non-linear. Additionally, with the presence of dynamics in billion-scale datasets, such as items are arriving in an online fashion, scalable and stream-based adaptive algorithms which can quickly update solutions instead of recalculating from scratch must be investigated. All of the aforementioned issues call for a scalable and stream-based active mining techniques to cope with enormous applications of non-submodular maximization in the era of big data. With the society's growing dependence on the cyberspace and computer technologies, the premium placed on the intelligent big data analysis for many emerging applications. Therefore, the success of this project has a high impact in almost any field that needs lightweight and near-optimal big data analysis. The findings of this project will also enrich the research on network science, graph theory, optimization, and big data analysis. In addition to creating new courses, undergrad and high school students will be involved in hands-on activities over the experimental platform. Outreach events targeted at under-represented groups and K-1 This project develops a theoretical framework together with highly scalable approximation algorithms and tight theoretical performance bound guarantees for the class of non-submodular and non-linear optimization. In particular, the project lays the foundation for the novel data mining techniques, suitable to the new era of big data with emerging applications, as well as advance the research front of stochastic and stream-based algorithm designs, with several key innovations: 1) Rigorous mathematical techniques to analyze and design highly scalable approximation algorithms to the class of non-monotonic, non-submodular maximization, which underlies many emerging applications. 2) Attempt a new research direction by bridging the non-linear optimization and the combinatorial optimization, thereby bringing the new angles for the study of non-submodular optimization as well as getting deeper understanding of the problem structures. 3) Novel stream-based active mining at scale for multiple applications, focused on the two general models which unify many optimization problems in the domain of online social networks and privacy. It also provides a novel theoretical framework for adaptive non-submodular maximization, which has not been studied in the literature. 4) Extensive evaluation through a combination of various tools and methods, including the real-world datasets and applications that will bridge the gap between theory and practice. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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