@article {1973201,
	title = {Topological Analysis of Criminal Activity Networks: Enhancing Transportation Security},
	journal = {IEEE Transactions on Intelligent Transportation Systems},
	volume = {10},
	year = {2009},
	month = {2009},
	pages = {83 - 91},
	abstract = {The security of border and transportation systems is a critical component of the national strategy for homeland security. The security concerns at the border are not independent of law enforcement in border-area jurisdictions because the information known by local law enforcement agencies may provide valuable leads that are useful for securing the border and transportation infrastructure. The combined analysis of law enforcement information and data generated by vehicle license plate readers at international borders can be used to identify suspicious vehicles and people at ports of entry. This not only generates better quality leads for border protection agents but may also serve to reduce wait times for commerce, vehicles, and people as they cross the border. This paper explores the use of criminal activity networks (CANs) to analyze information from law enforcement and other sources to provide value for transportation and border security. We analyze the topological characteristics of CAN of individuals and vehicles in a multiple jurisdiction scenario. The advantages of exploring the relationships of individuals and vehicles are shown. We find that large narcotic networks are small world with short average path lengths ranging from 4.5 to 8.5 and have scale-free degree distributions with power law exponents of 0.85{\textendash}1.3. In addition, we find that utilizing information from multiple jurisdictions provides higher quality leads by reducing the average shortest-path lengths. The inclusion of vehicular relationships and border-crossing information generates more investigative leads that can aid in securing the border and transportation infrastructure.},
	keywords = {Accounting, BIS},
	url = {http://dx.doi.org/10.1109/TITS.2008.2011695},
	author = {Kaza,Siddharth and Xu,Jennifer and Marshall,Byron and Chen,Hsinchun}
}
@article {1972886,
	title = {Complex problem solving: A case study on identity matching based on social contextual information},
	journal = {Journal of the Association for Information Systems: Special Issue Enid Mumford{\textquoteright}s Contribution to Information Systems Theory and Theoretical Thinking},
	volume = {8},
	year = {2007},
	month = {2007},
	pages = {Article 31},
	keywords = {BIS},
	author = {Xu,Jennifer and Wang,Alan G. and Li,Jiexun and Chau,Michaul}
}
@article {1980891,
	title = {Genescene: Biomedical text and data mining},
	year = {2003},
	month = {2003},
	address = {Houston, TX, USA},
	keywords = {BIS},
	author = {Leroy,Gondy and Chen,Hsinchun and Martinez,Jessie and Eggers,S and Falsey,R and Kislin,K and Huang,Zan and Li,Jiexun and Xu,Jennifer and McDonald,Daniel and Ng,Gavin}
}
