Owing to a wide variety of deployment of GPS -enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors. In view of skewed distribution property and evolving nature of trajectory data, and on-the-fly detection requirement over distributed streams, we first design a high-efficiency outlier detection solution. It consists of identifying abnormal trajectory fragment and exceptional fragment cluster at the remote sites and then detecting abnormal evolving object at the coordinator site. Further, given that outlier detection accuracy would be damaged due to using inappropriate proximity thresholds or a few trajectory data not having sufficient neighbors at the remote sites, we extract proximity thresholds of different regions and spatial context relationship of each region from historical data to improve the precision. Built upon this is an improved version consisting of off-line modeling phase and on-line detection phase. During the on-line phase, the proximity thresholds that are derived from historical trajectories during the off-line phase are leveraged to assist in detecting abnormal trajectory fragments and exceptional fragment clusters at the remote sites. Additionally, at the coordinator site, the detection results of some remote sites can be refined by incorporating those of other remote sites with neighborhood relationship. Extensive experimental results on real data demonstrate that our proposed methods own high detection validity, less communication cost and linear scalability for online identifying outliers over distributed trajectory streams.

The rapid development of steel logistics industry still has not effectively address such issues as truck overload and order overdue as well as cargo overstock. One of the reasons lie in limited number of trucks for transporting large scale cargos. More importantly, traditional methods attend to distribute cargos to trucks with the aim of maximizing the loading of each truck. But they ignore the priority level of orders and the expiration date of cargos stored in the warehouses, which have critical influences on profits of steel logistics industry. Hence, it necessitates an appropriate cargo distribution mechanism under the precondition of limited transportation capacity resources, to guarantee the maximization of delivery proportion for high-priority cargos. Recently, tremendous logistics data has been produced and are being in constant increment hourly in steel logistics platform. However, there is no existing solution to transform such data into actionable scheme to improve cargo distributing effectiveness. This paper puts forward a system implementation of smart cargo loading plan decision framework (SCLPD for short) for steel logistics industry. Through analysis on numerous real data cargo loading plan and inventory of warehouse, some important rules related to cargo distribution process are extracted. Additionally, consider that different amounts of trucks arriving in different time periods, based on adaptive time window model, a two- layer searching mechanism consisting of a genetic algorithm and A* algorithm is designed to ensure global optimization of cargo loading plan for the trucks in all time periods. In our demonstration, we illustrate the procedure of matching for cargos and trucks in various time windows, and showcase the comparison experimental results between the traditional method and SCLPD by the measurement of delivery proportion for high- priority cargos. The effectiveness and practicality of SCLPD enables efficient cargo loading plan generation, to meet the real- world requirements from steel logistics platform.