This article presents a data management solution based on the data distribution service （DDS） communication model. The basic DDS communication model consists of a unidirectional data exchange where applications that publish data ＇push＇ the relevant data, which is updated to the local caches of co-located subscribers to the data [1]. DDS has no specified center node to forward data packets and maintain the communication data. This type of publish-subscribe （P/S） model presents integrity and consistency challenges in data management. Unlike peer-to-peer （P2P） distributed storage, DDS applications have a hard real-time environment and fewer data features, and the core problem is ensuring the integrity and consistency of data in distributed systems under this hard real-time environment. This article begins with a brief introduction of the communication model used by DDS, then analyzes persistent data management problems caused by such model, and provides an appropriate solution to these problems. This solution has been implemented in a prototype system of the real-time service bus （RTSB） of Tsinghua University.
在复杂网络的传播模型研究中,如何发现最具影响力的传播节点在理论和现实应用中都有重大的意义.目前的研究一般使用节点的度数、紧密度、介数和K-shell等中心化指标来评价影响力,这种方法虽然简单,但是由于它们仅利用了节点自身的内部属性,因而在评价影响力时精确度并不高,普遍性适用性较弱.为了解决这个问题,本文提出了KSC(K-shell and community centrality)指标模型.此模型不但考虑了节点的内部属性,而且还综合考虑了节点的外部属性,例如节点所属的社区等.然后利用SIR(susceptible-infected-recovered)模型对传播过程进行仿真,实验证明所提出的方法可以更好地发现最具有影响力的节点,且可适用于各种复杂网络.本文为这项具有挑战性研究提供了新的思想和方法.
