Traditional decision support does not consider real-time and mobility of user terminals, leading to inconvenience for mobile users to access database and make decision. Data play an important role in decision-making. The paper presents mobile data services architecture, discusses device-related data, user-related data and historical data in mobile process, and describes mobile decision support architecture related to data accessed in mobile terminal in the end.

No abstract

The Yellow Sea Green Tide, which occurs repeatedly every year, has a significant impact on China's ecological environment and economy. At present, resource scheduling in the process of emergency response to green tide disasters depends more on disposal experience. In order to improve the reliability and efficiency of decision-making, we propose a resource scheduling model, which is used to optimize the allocation of resources. First, determine the main influencing factors, including the details of salvage objects and available resources. Next, with the maximum salvage efficiency as the goal, the resource scheduling model of marine green tide disaster is established. An improved particle swarm optimization (SAPSO) is used to solve the model. Finally, a test case is taken as an example for experimental verification. The results show that, compared with PSO algorithms, the resource scheduling scheme obtained by SAPSO algorithm is better. The model proposed in this paper can help decision-makers to make effective resource scheduling schemes.