The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.
With regard to the inefficient application of a food processing information system due to shortage of the knowledge acquisition measure and self-updating function of knowledge, a method of constructing an online aided decision making knowledge base for quality and security of food processing, based on regular expression, is discussed in the paper. Firstly, the method establishes an online aided decision making knowledge base for quality and security of food processing based on regular expression; and then an automatic knowledge inference engine is applied to update the knowledge in the base, combined with industry experts’ experience knowledge. Continuous deriving of food processing knowledge can be realized based on the inference engine. The research will greatly enhance the efficiency and applicability of obtaining knowledge from an online aided decision making system for quality and security of food processing.