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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kretz, D.R. Paulk, R.W. |
| Copyright Year | 2010 |
| Description | Author affiliation: Raytheon Intelligence and Information Systems, Predictive Analytics Technology Center, Garland, TX (Kretz, D.R.; Paulk, R.W.) |
| Abstract | Every day, millions of people cross international borders by air or sea. A nation's ability to identify and neutralize threats posed by travelers depends heavily on an accurate and proactive methodology for establishing traveler identity. Lacking both a common reference model and a globally unique identification scheme, the “system” must confront the problem of multiple distinct or imprecise references to each single real-world entity when analyzing vast amounts of data. Additionally, the emergent patterns of relationships between entities are often complex, fuzzy, and/or novel. Methodologies that depend solely on exact or approximate name matches are subject to defeat simply by using aliases, and are typically prone to high levels of false positives. In order for identity resolution to be effective, information from multiple, diverse sources must be analyzed (e.g., biometric, biographic, social, etc.). Furthermore, identity analysis must be a continuous process that enables accurate pre-assessment when travel intentions first become known. To that end, this paper presents an identity resolution methodology called Collective Identity Resolution. |
| Starting Page | 308 |
| Ending Page | 313 |
| File Size | 2165538 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424460472 |
| e-ISBN | 9781424460489 |
| DOI | 10.1109/THS.2010.5655036 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | US Department of Energy risk assessment Correlation Clustering algorithms identity Feature extraction entity resolution Risk management Data mining Facebook |
| Content Type | Text |
| Resource Type | Article |
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