ID 原文 译文
45356 采用非线性时间序列分析方法对网络访问时间演化序列混沌辨析， Using nonlinear time sequences analysis method to identify the Chaos characteristics of network traveling time evolution sequences.
45357 结果表明其时序演化具有混沌特征。 The results show that their timing evolution has Chaos characteristics.
45358 在此基础上，引入 Logistic 方程建立网络传播行为预测模型，并用粒子群优化算法对模型参数取优， Based on this, the Logistic equation was lead to establish network transmission behavior prediction model, and particle swarm optimization (PSO) was used to optimize model parameters.
45359 用 4个监测点的网络访问时间序列对模型进行实验，从准确性和可用性这 2 个方面对模型进行评价，结果表明，短期内该模型能够对网络传播行为做出准确预测， The model by the network traveling time sequences of four monitoring points was experimented, evaluated it from accuracy and availability, the results show that the model can predict network transmission behavior accurately in the short term.
45360 在一段时期内，可作为网络行为演化预测的工具。 It can be used as a tool for predicting the network behaviors’ evolution in a period of time.
45361 目前，各领域对图数据的分析、应用需求日益增加， The demand for the analysis and application of graph data in various fields is increasing day by day.
45362 且对结构复杂、耦合度高的大规模图数据的管理面临着速度低下和空间开销大的双重挑战。 The management of large-scale graph data with complicated structure and high degree of coupling faces two challenges: one is querying speed too slow, the other is space consumption too large.
45363 面对图数据管理中查询耗时高和空间占比大的难题，提出一种图数据二级索引压缩算法——GComIdx。 Facing the problems of long query time and large space occupation in graph data management, a two-level index compression algorithm named GComIdx for graph data was proposed.
45364 该算法利用有序的键值（Key-Value）结构将相关节点和边尽可能地以相邻的方式存储，并为高效的属性查询和邻居查询分别构造二级索引和 hash 节点索引。 GComIdx algorithm used the ordered Key-Value structure to store the associated nodes and edges as closely as possible, and constructed two-level index and hash node index for efficient attribute query and neighbor query.
45365 此外，为了节省存储空间，GComIdx算法采用压缩算法来降低图数据磁盘空间占用率。 Furthermore, GComIdx algorithm used a graph data compressed technology to compress the graph data before it directly stored in hard disk, which could effectively reduce the storing space consumption.