Transactions Behavior Analysis for Internet Auction Fraud

People often enmesh the Internet auction frauds which damage the benefits of Internet market and threaten transactions security. This research applies social network analysis and data mining to extract characteristic features from two random collected transaction datasets of Yahoo auction site. One dataset is used to construct prediction model and another is treated as validation. The average accuracy ratio of proposed model is at least 90%. The findings are: (1) the abnormal accounts involve circular transaction; (2) fraud accounts can accumulate higher positive reputations in very short time from its circular transaction and rarely play key nodes in transaction network

The design and evaluation of multilingual social media portal: Working paper series--11-09

Web 2.0 has created a large amount of user-generated content from online social media such as forums, blogs, social-networking sites, etc. As a result, the volume of social media data has been growing exponentially. Such user-generated data contains valuable information about people's ideas and opinions toward products, services or social/political issues. However, certain issues related to social media data, including data integration and multilingual issues, are difficult to analyze with this type of data. This paper develops a general framework of social media portals. It can collect and incorporate data from heterogeneous social media sites, and provide unified data integration, search, and multilingual translation supports. To evaluate the performance of the proposed framework, a prototype system, the Dark Web Forums Portal, is built using social media from homeland security-related data sources. The user evaluation results show that, compared with alternative ways of searching and understanding multilingual social media content, the portal can help users locate information more quickly and effectively. Users also perceived significantly higher system quality, ease of use, usefulness, satisfaction, intention to use when using the portal. The results demonstrated the advancement of the proposed system framework

PutMode: prediction of uncertain trajectories in moving objects

Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life. Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs. Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy

PutMode: Prediction of uncertain trajectories in moving objects databases

Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life. Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs. Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy. © 2009 Springer Science+Business Media, LLC.link_to_subscribed_fulltex