Contributions published at Business Intelligence (Daning Hu)

Workflows have been widely used for coordinating structured processes, whereas group support systems are used to facilitate ad hoc and unstructured collaboration activities. In many business settings, it is beneficial to use both workflows and group support systems. However, how to integrate workflow systems and group support systems have not been well studied in the literature. In this paper, we propose a scalable middleware framework, namely event management system, which can support high-degree decoupling between workflow and groupware. The event management architecture and its main functionalities as well as the implementation techniques are presented. We delineate the applicability of event-based integration by comparing with other integration paradigms.

null

null

"Dark Networks" refer to various illegal and covert social networks like criminal and terrorist networks. These networks evolve over time with the formation and dissolution of links to survive control efforts by authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. These findings provide insights into the link formation processes and the resilience of dark networks. They also can be used to help authorities predict co-offending in future crimes.

Criminal networks evolve over time with the formation and dissolution of links to survive control efforts by government authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. The findings shown in this poster can help government authorities automatically predict co-offending relationships in future crimes.

Academic nanoscale science and engineering (NSE) research provides a foundation for nanotechnology innovation reflected in patents. About 60% or about 50,000 of the NSE-related patents identified by “full-text” keyword searching between 1976 and 2004 at the United States Patent and Trademark Office (USPTO) have an average of approximately 18 academic citations. The most cited academic journals, individual researchers, and research articles have been evaluated as sources of technology innovation in the NSE area over the 28-year period. Each of the most influential articles was cited about 90 times on the average, while the most influential author was cited more than 700 times by the NSE-related patents. Thirteen mainstream journals accounted for about 20% of all citations. Science, Nature and Proceedings of the National Academy of Sciences (PNAS) have consistently been the top three most cited journals, with each article being cited three times on average. There is another kind of influential journals, represented by Biosystems and Origin of Life , which have very few articles cited but with exceptionally high frequencies. The number of academic citations per year from ten most cited journals has increased by over 17 times in the interval (1990–1999) as compared to (1976–1989), and again over 3 times in the interval (2000–2004) as compared to (1990–1999). This is an indication of increased used of academic knowledge creation in the NSE-related patents.

It is very important for us to understand the functions and structures of terrorist networks to win the battle against terror. However, previous studies of terrorist network structure have generated little actionable results. This is mainly due to the difficulty in collecting and accessing reliable data and the lack of advanced network analysis methodologies in the field. To address these problems, we employed several advance network analysis techniques ranging from social network analysis to Web structural mining on a Global Salafi Jihad network dataset collected through a large scale empirical study. Our study demonstrated the effectiveness and usefulness of advanced network techniques in terrorist network analysis domain. We also introduced the Web structural mining technique into the terrorist network analysis field which, to the best our knowledge, has never been used in this domain. More importantly, the results from our analysis provide not only insights for terrorism research community but also empirical implications that may help law-reinforcement, intelligence, and security communities to make our nation safer.