Incremental View Computation Model for Object-Oriented Information

We introduce a model to implement incremental update of views. The principle is that unless a view is accessed, the modification related to the view is not computed. This modification information is used only when views are updated. Modification information is embodied in the classes (including inheritance classes and nesting classes) that derive the view. We establish a modify list consisted of tuples (one tuple for each view which is related to the class) to implement view update. A method is used to keep views from re-update. Key words object-oriented database - incremental computation - view-computation - engineering information system CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China(60235025)Biography: Guo Hai-ying (1971-), female, Ph. D, research direction: CAD and engineering information system.

A New Method Based on Evolutionary Algorithm for Symbolic Network Weak Unbalance

The symbolic network adds the emotional information of the relationship,that is,the“+”and“-”information of the edge,which greatly enhances the modeling ability and has wide application in many fields.Weak unbalance is an important indicator to measure the network tension.This paper starts from the weak structural equilibrium theorem,and integrates the work of predecessors,and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm.Experiments on the large symbolic networks Epinions,Slashdot and WikiElections show the effectiveness and efficiency of the proposed method.In EAWSB,this paper proposes a compression-based indirect representation method,which effectively reduces the size of the genotype space,thus making the algorithm search more complete and easier to get better solutions.

An effective framework for asynchronous incremental graph processing

Although many graph processing systems have been proposed, graphs in the real-world are often dynamic. It is important to keep the results of graph computation up-todate. Incremental computation is demonstrated to be an efficient solution to update calculated results. Recently, many incremental graph processing systems have been proposed to handle dynamic graphs in an asynchronous way and are able to achieve better performance than those processed in a synchronous way. However, these solutions still suffer from sub-optimal convergence speed due to their slow propagation of important vertex state (important to convergence speed) and poor locality. In order to solve these problems, we propose a novel graph processing framework. It introduces a dynamic partition method to gather the important vertices for high locality, and then uses a priority-based scheduling algorithm to assign them with a higher priority for an effective processing order. By such means, it is able to reduce the number of updates and increase the locality, thereby reducing the convergence time. Experimental results show that our method reduces the number of updates by 30%, and reduces the total execution time by 35%, compared with state-of-the-art systems.