No abstract

No abstract

The express delivery industry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition framework to resolve such difficulty, which synthetically utilize intelligent inernet of things (IoT), semantic web and complext event processing (CEP) technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support from multidimensional space.

The resources are divided into curable and non-curable morphologies to improve the efficiency of virtual computing systems, and we determine the quantities of such two morphologies based on resources required and resources that system owes. In the environment of tasks presenting random distribution, we describe the trend of resources required and compute the range interval of curable morphology by theoretical analysis. It is concluded that the amounts of tasks have great effect on resources required and the experiments show that the trend of decision variables in our numerical example is consistent with the theory discussed. The decision making methodology presented in this paper has some significance to the management of resources in virtual computing systems.

No abstract