基于信息网模型的动态数据划分策略

为了满足大规模数据管理与查询的需要,设计并开发了基于信息网模型INM(Information Networking Model)的分布式并行数据库管理系统。分布式环境下数据的划分方式将影响系统的可扩展性和查询分析效率。根据信息网模型的数据结构和查询特性,设计一种轻量级的数据动态划分方法。该方法结合数据的水平分割和垂直分割,以INM对象为单位,未存储过的数据对象直接划分到当前操作节点,并记录数据对象的存储位置,否则根据数据对象的历史位置信息将其划分到不同的存储节点。同时,单个INM对象可能由于其包含的关联对象增多到一定程度成为大对象,而对系统的性能造成影响,因此将此类大对象分割成多个小对象,并按照一定的策略划分到不同节点进行存储。集群中的每个处理节点被赋予一个负载阈值。随着数据量的增加,如果超过负载阈值则增加新的机器,保证系统的可扩展性和各个处理节点数据量的均衡。实验结果证明,该方法能够保障系统良好的可扩展性,同时提高数据的查询分析效率。

面向信息网模型的高可扩展性分布式事务处理机制

为应对数据规模的快速增长,许多数据库管理系统提供了横向水平扩展的特性,但为保证扩展后的性能,大多不能自动维护数据间关联的强一致性。为此,设计一种高可扩展性的分布式事务处理机制。基于信息网模型的语义性和存储特点,根据所接收事务之间的关联度动态控制组提交粒度,从而利用缓存提高吞吐量。同时在子节点和备份节点同时执行相同的事务操作,而不是发送日志进行同步,以提高响应速度。实验结果表明,与两阶段提交协议相比,该机制在保证强事务特性的同时,能够获得近似线性的高扩展性,在系统吞吐量和响应时间方面也有较大改善。

基于信息网模型的Web实体语义信息搜索平台

Web上实体信息过于分散且缺乏语义,传统基于关键词匹配的搜索引擎往往因缺少上下文等语义信息,无法搜索到精确的结果。为了对Web数据进行精确查找,使用信息网模型(INM)对Web数据进行语义表示和建模,将实体的所有语义信息组织在一个对象中,快速获取实体完整的语义信息。基于INM构建复杂语义数据库,设计实现一个可对教育领域相关实体信息进行精确搜索的TLDW系统。实验结果表明,该系统初次查询时间均在100 ms内,其搜索结果包含实体的上下文关系等多种语义信息,缓存优化后的搜索结果可在20 ms内完成。

面向OLGP的多维信息网络数据仓库模型设计

信息网络的出现使信息由简单的数值型数据演化成较复杂的图网络结构。如何对基于图的信息网络数据进行良好的组织和存储成为一个亟待解决的问题。利用维建模的方法对基于图的信息网络数据进行模型设计,提出了多维信息网络仓库模型。该模型由边事实表、节点事实表、信息维连接属性表以及拓扑维节点属性表组成,能够为在线图处理提供底层的数据平台。实验表明该模型在消除冗余、查询时间、存储空间上均较泛关系表有明显优势。新模型在1.25万篇ACM论文上的查询时间稳定在几十毫秒,较泛关系表的查询时间约减少一个数量级。在空间性能上,随着论文数量的增加,该模型存储空间开销的增长速度远小于泛关系表的增长速度。

基于信息网模型的分布并行多连接查询优化

在分布式集群系统中,数据根据划分算法存储在集群的各个节点,这为涉及大量连接操作的复杂查询带来了昂贵的网络开销。针对该问题,基于信息网模型INM(Information Network Mode),提出最小通信量查询划分算法和多目标查询优化算法。其中查询划分算法将复杂查询划分成多个PWOC(parallelizable without communication)子查询,所有子查询可近似无通信地并行执行。多目标优化算法将子查询作为查询计划的基本操作,并将并行性和通信代价同时作为驱动目标,以传统多目标加权算法结合贪心策略作为评估依据生成查询计划树。最后,系统基于TPC-H基准生成测试数据,将原始算法与优化算法进行了对比实验,结果表明优化算法可以极大提高复杂查询的效率。

面向信息网模型的动态数据划分算法

针对分布式信息网数据库管理系统中因跨节点的复杂查询带来的昂贵通信开销,提出一种基于信息网模型和查询的数据动态划分算法。该算法根据信息网模型的关系特性和历史关系信息得到数据之间的初始关联,并结合历史查询信息挖掘数据之间的潜在关联,将关联性较强的数据动态调整到同一个处理节点上,使复杂查询跨节点的数量减少。最后,在标准合成数据集Wat Div上进行大量的实验评估。实验结果表明:在保证节点之间的对象个数和关系对占比负载均衡的情况下,该算法在周期内的查询时间与一致性哈希算法相比缩短了35%~55%,并将多个周期相同查询的时间波动控制在5%~10%,保证了复杂查询的稳定性。

信息网数据库管理系统规则的设计与实现

为了实现信息网数据库管理系统INMDBMS(Information Network Database Management System)的逻辑推理功能,提出了适用于INMDBMS的信息规则语言IRL(Information Rule Language)用于规则的表示。总结Datalog语言中几种传统的递归查询算法,同时结合INMDBMS的数据模型特点,设计并实现了IGQA(INMDB Goal_driven Recursive Query Algorithm)作为IRL规则语言的递归查询算法。IGQA以深度优先、回退的方式来实现递归查询。在真实数据集上的实验表明,当数据规模增长时,IGQA具有较高的执行成功率和较好的执行时间稳定性。

雷达信息网络化与可视化的实现

随着信息技术的迅速发展,传感器信息的网络化和可视化成为一种必然趋势。而雷达作为战场情报的主要来源,实现其情报信息的网络化和可视化成为必需。本文从这个角度出发,提出雷达信息网络化和可视化的总体设计思路。同时构建了雷达信息网模型和雷达信息数据库,详细分析了以VC++作为开发平台实现雷达信息可视化的具体过程。

信息网数据库管理系统的查询优化

查询是信息网数据库管理系统的重要组成部分。反向查询是一种被广泛应用,并且十分耗时的查询策略,因为在反向查询中对象名未知,在匹配路径之前需要通过路径反向查得对象。针对反向查询,提出了优化算法,从最后一条有价值的路径单元开始反向查询,利用路径上提供的所有信息,将搜索空间限制至最小,使得花费在路径匹配上的时间减少,查询效率得以提升。最后,原始算法和优化算法进行了对比实验,结果表明了优化算法的优越性。

FC-InfoNet-Warehouse:信息网络数据仓库建模及实现

信息网络是一种对复杂网络进行高度一致性抽象、用于处理图数据的新型概念。为了更加灵活高效地读取数据,提升研究效率,提出一种以图为主题结合信息网络与数据仓库优势的FC信息网络数据仓库模型。实验表明该模型在查询效率,存储空间及查询灵活性等方面较前人所提EN信息网络数据仓库模型均具有明显优势。

基于Petri网的信息系统建模

传统的信息系统建模技术主要有如以数据流图 (DFD)为手段的功能建模和以对象图为手段的对象建模 ,它们多强调的是系统功能的实现 ,对系统性能的分析和评价能力则显得过于薄弱。本文提出的一种基于Petri网的信息系统建模技术 ,作为从实际问题到Petri网的桥梁 ,对于系统的实现是一种很好的模拟 ,它可以充分利用网论强大的图形表达能力和严谨的数学分析理论对信息系统的各种综合性能进行描述和分析 ,具有一定应用价值。

一种恶意软件传播的离散概率模型

复杂网络理论为恶意软件传播的研究提供了新的思路和方法.本文针对恶意软件的实际传播机制,提出一种新的离散概率DP-SI模型,该模型可适用于任意网络拓扑.同时提出了一种节点信息网络模型方法,为大规模复杂网络及复杂网络上的传播动力学的仿真,以及离散传播动力学模型的建立,提供了有效的研究平台.仿真结果表明本模型比传统模型更接近现实,对恶意软件的控制具有一定指导意义.

INMDB中复合事件监测机制的设计与实现

对于大多数主动数据库来说,复合事件监测始终是个难题。介绍信息网模型INM(Information Network Model)数据库管理系统中的复合事件监测机制,详细描述利用事件树模型监测复合事件的思想,并提供具体的算法实现。经分析,该算法在运行效率和空间占用上均比常见的有限自动机和Petri网有着更好的表现。

探讨BIM技术在智慧矿山建设中的应用

在智慧矿山建设过程中,BIM技术在其中发挥着不可替代的作用。因此,为实现我国社会更快进步,要将BIM技术优势充分发挥出来,这样才能保证智慧矿山质量,不断提升我国矿山工程质量,实现我国矿山工程的可持续发展。论文针对BIM技术在智慧矿山建设中的应用进行阐述。

Social Network Information Propagation Model Based on Individual Behavior

In this paper, we discuss building an information dissemination model based on individual behavior. We analyze the individual behavior related to information dissemination and the factors that affect the sharing behavior of individuals, and we define and quantify these factors. We consider these factors as characteristic attributes and use a Bayesian classifier to classify individuals. Considering the forwarding delay characteristics of information dissemination, we present a random time generation method that simulates the delay of information dissemination. Given time and other constraints, a user might not look at all the information that his/her friends published. Therefore, this paper proposes an algorithm to predict information visibility, i.e., it estimates the probability that an individual will see the information. Based on the classification of individual behavior and combined with our random time generation and information visibility prediction method, we propose an information dissemination model based on individual behavior. The model can be used to predict the scale and speed of information propagation. We use data sets from Sina Weibo to validate and analyze the prediction methods of the individual behavior and information dissemination model based on individual behavior. A previously proposedinformation dissemination model provides the foundation for a subsequent study on the evolution of the network and social network analysis. Predicting the scale and speed of information dissemination can also be used for public opinion monitoring.

A Trajectory-Oriented, Carriageway-Based Road Network Data Model,Part 2:Methodology

This is the second of a three-part series of papers which presents the principle and architecture of the CRNM, a trajectory-oriented, carriageway-based road network data model. The first part of the series has introduced a general background of building trajectory-oriented road network data models, including motivation, related works, and basic concepts. Based on it, this paper describs the CRNM in detail. At first, the notion of basic roadway entity is proposed and discussed. Secondly, carriageway is selected as the basic roadway entity after compared with other kinds of roadway, and approaches to representing other roadways with carriageways are introduced. At last, an overall architecture of the CRNM is proposed.

The Fatherhood Premium： Mirage or Reality？ How Do Occupational Characteristics Matter

A differential fatherhood premium exists among selected groups of fathers of various socio-economic backgrounds. Besides marital status, residential arrangement, biological paternity, and other demographic dimensions, the mechanisms associated with fathers＇ structural characteristics of work that produce divergent labor market outcomes are less well understood. The authors leveraged the cases of productivity and specialization, family and responsibility, and compensating differentials and examined the impacts of work context and occupational characteristics on the pay gap between fathers and non-fathers Based on joint data from the 1997-2015 National Longitudinal Survey of Youth （NLSY97） and Occupational Information Network （O＇NET） （n -- 37,138）, the authors used fixed-effects models to estimate the associations between job features and the fatherhood premium. The regression results showed that occupations entailing competition and requiring on-the-job training are associated with more wage bonus; occupations offering inflexible schedule are associated with less wage gain These work-related characteristics further emphasize fathers＇ work structure and context, human capital accumulation, and work-family conflict. Overall, these findings are consistent with the theoretical perspectives of productivity and specialization and compensating differentials, adding new evidences to the structural explanations of the fatherhood premium.

Model Warehouse

This paper puts forward a new conception:model warehouse,analyzes the reason why model warehouse appears and introduces the characteristics and architecture of model warehouse.Last,this paper points out that model warehouse is an important part of WebGIS.

Chinese word segmentation with local and global context representation learning

A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.

Research on E-mail Communication Network Evolution Model Based on User Information Propagation

E-mail communication network evolution model based on user information propagation is studied. First, mathematical representation of weighted e-mail communication network is proposed, and network center parameters of Enron dataset and the distribution of node degree and strength are analyzed. Then, some rules of e-mail communication network evolution are found. Second, the model of e-mail information propagation is described, and e-mail communication network evolution model based on user information propagation is proposed. Lastly, the simulation proves the correctness of the distribution characteristic of degree and strength of the model proposed and then verifies that the model proposed is closer to the real situation of e-mail communication network through parameter comparison. This research provides the basis for other researches on social network evolution and data communication.