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Research Spending & Results

Award Detail

Awardee:OHIO STATE UNIVERSITY, THE
Doing Business As Name:Ohio State University
PD/PI:
  • Yingbin Liang
  • (609) 658-1330
  • liang.889@osu.edu
Award Date:11/30/2017
Estimated Total Award Amount: $ 184,277
Funds Obligated to Date: $ 184,277
  • FY 2016=$184,277
Start Date:10/01/2017
End Date:06/30/2019
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CIF: Small: Collaborative Research: Network Event Detection with Multistream Observations
Federal Award ID Number:1801855
DUNS ID:832127323
Program:COMM & INFORMATION FOUNDATIONS
Program Officer:
  • John Cozzens
  • (703) 292-8910
  • jcozzens@nsf.gov

Awardee Location

Street:Office of Sponsored Programs
City:Columbus
State:OH
ZIP:43210-1016
County:Columbus
Country:US
Awardee Cong. District:03

Primary Place of Performance

Organization Name:Ohio State University
Street:Dreese Lab
City:Columbus
State:OH
ZIP:43210-1016
County:Columbus
Country:US
Cong. District:03

Abstract at Time of Award

The goal in network event detection is to detect the existence of a set of nodes over a large network whose observations reflect the occurrence of an unusual event. Existing studies of network event detection have been mainly from two perspectives. The first is data-driven without assuming any underlying statistical model, and is typically applicable to more general data sets, but may not come with performance guarantees. The second perspective is model-driven, with certain statistical distributions (e.g., Gaussian) assumed for the data, and usually comes with performance guarantees, but may be limited to applications where the data fit the model. The goal in this project is to explore a framework for network event detection that unifies a wide range of event detection problems, in which the data are assumed to be governed by some underlying statistical distributions, but is data-driven in the sense that little is assumed a priori about the distributions. The developed detection approaches and statistical tools have a wide range of applications, including fraud detection, clinical trials, medical diagnosis, high-frequency trading, voting irregularity analysis, and network intrusion. A comprehensive approach to general network event detection problems is developed in this project through the exploration of three thrusts: (i) detection of (unstructured) point events, (ii) detection of graph-based structured events, and (iii) sequential and quickest detection of dynamically evolving graph structures. The performance of the designed tests is characterized in terms of the probability of detection error and the rate at which this error goes to zero. Various fundamental issues are addressed, including non-i.i.d. data streams, as well as the interplay between network size, event size, sample size, and data dimension.

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