Towards sparse matrix operations:graph database approach for power grid computation

The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)technology.The“source-grid-load-storage”architecture of a new power system requires PIoT to have a stronger multi-source heterogeneous data fusion ability.Native graph databases have great advantages in dealing with multi-source heterogeneous data,which make them suitable for an increasing number of analytical computing tasks.However,only few existing graph database products have native support for matrix operation-related interfaces or functions,resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids.In this paper,the matrix computation process is expressed by a strategy called graph description,which relies on the natural connection between the matrix and structure of the graph.Based on that,we implement matrix operations on graph database,including matrix multiplication,matrix decomposition,etc.Specifically,only the nodes relevant to the computation and their neighbors are concerned in the process,which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation.Based on the graph description,a series of power grid computations can be implemented on graph database,which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database.It promotes the efficiency of PIoT when handling multi-source heterogeneous data.An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines.The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.

Query Optimization Framework for Graph Database in Cloud Dew Environment

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance systems.For performance enhancement,compression and query optimization are the two most factors.Compression reduces the volume of data,whereas query optimization minimizes execution time.Compressing the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

Query Acceleration of Graph Databases by ID Caching Technology

In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations which will consume great computer resources. Traditional graph database caching technology(GDCT)needs a large memory to store data and has the problems of serious data consistency and low cache utilization. To address these issues, in the paper we propose a new technology which focuses on ID allocation mechanism and high-speed queries of ID on graph databases. Specifically, ID of the query result is cached in memory and data consistency is achieved through the real-time synchronization and cache memory adaptation. In addition, we set up complex queries and simple queries to satisfy all query requirements and design a mechanism of cache replacement based on query action time, query times, and memory capacity, thus improving the performance furthermore.Extensive experiments show the superiority of our techniques compared with the traditional query approach of graph databases.

Design and Implementation of NBA Playoff Prediction Method Based on ELO Algorithm and Graph Database

With the globalization of NBA, all eyes on the NBA playoffs are around the world. Ones celebrate the winning of their team which they like. Especially, NBA fans keep on predicting the playoffs game results. However, prediction of winning probability of teams in NBA playoffs is challenging. In order to meet the challenges, we proposed a method using ELO algorithm for prediction and leveraging Graph Database, Neo4j, for implementation. Experiment results show that, the design and implementation of the prediction system could work to some degree.

MillenniumDB:An Open-Source Graph Database System

In this systems paper,we present MillenniumDB:a novel graph database engine that is modular,persistent,and open source.MillenniumDB is based on a graph data model,which we call domain graphs,that provides a simple abstraction upon which a variety of popular graph models can be supported,thus providing a flexible data management engine for diverse types of knowledge graph.The engine itself is founded on a combination of tried and tested techniques from relational data management,state-of-the-art algorithms for worst-case-optimal joins,as well as graph-specific algorithms for evaluating path queries.In this paper,we present the main design principles underlying MillenniumDB,describing the abstract graph model and query semantics supported,the concrete data model and query syntax implemented,as well as the storage,indexing,query planning and query evaluation techniques used.We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph,where we find that it outperforms other popular persistent graph database engines(including both enterprise and open source alternatives)that support similarqueryfeatures.

A large scale power communication network simulation system based on big graph database

The power communication network can be abstracted as a graph based on its topology. In this paper, we propose an approach to conduct simulations of power communication network based on its graph representation. In particular, the nodes and edges in the graph refer to the ports and channels in the grid topology. Different applications on the grid can be transformed into queries over the graph. Hence, in this paper, we build our grid simulation model based on the Neo4 j graph database. We also propose a fault extension algorithm based on predicate calculus. Our experiment evaluations show that the proposed approach can effectively improve the efficiency of the power grid.

Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.

基于分布式图数据库JanusGraph的高可用服务架构研究

目前,图数据广泛应用于金融科技、智能问答、智慧城市等多个领域。图数据库作为图数据的存储介质,技术发展迅猛。随着数据量的爆发式增加,图中顶点和边的数量随之增加,所需要的存储和计算能力远超出单台机器所能提供的能力,因此分布式图数据库成为图数据库发展的必然方向。JanusGraph作为一种开源图数据库,具备可拓展节点数量的分布式存储能力,支持海量点边图数据的存储,在分布式环境下的图查询和图遍历都有着良好的性能。虽然JanusGraph支持数据的分布式存储,但是其服务本身Janus-Server并未支持高可用。文章提出一种基于JanusGraph的高可用服务架构,补足JanusGraph的这一缺陷,提升JanusGraph的高可用性。

面向电商领域的旗袍知识图谱构建与实证研究

为挖掘旗袍服饰要素的关联关系与隐藏价值,构建一个基于电商数据的细粒度旗袍知识图谱,并开展实证研究。旗袍知识图谱采用自底向上逐层构建的方法,首先根据先验知识设计细粒度本体模型,为后续工作提供逻辑基础;然后采集并预处理原始数据,通过知识抽取、知识融合规范化数据,进行实例填充,完成数据层设计;最后将数据存储到图数据库,实现可视化。在此基础上,围绕旗袍、服饰要素以及用户人群开展深度挖掘、知识推理,借助图分析算法在社区检测、相似度和要素关联上展开实证研究。结果表明:旗袍知识图谱蕴含服饰要素之间隐藏的关系,能够挖掘内在的语义信息;旗袍服饰要素和用户人群之间存在一定的知识关联性,进而辅助设计与销售决策。

中国专利奖视角下的颠覆性技术识别分析

为充分利用专利结构化数据和文本数据,实现准确有效的颠覆性技术识别,以中国专利奖为切入点,提出基于图神经网络的颠覆性技术识别框架。首先以获得中国专利奖的授权发明专利定义颠覆性技术,解决技术定义难的问题;接着使用Neo4j图数据库构建异质有向图,存储专利多重关系数据和方向信息,解决关系数据利用率低的问题;最后使用关系图卷积神经网络(R-GCN)模型进行训练,实现颠覆性技术识别,解决识别效果不佳的问题。研究表明以获得中国专利奖的专利技术直接定义颠覆性技术是合理且可靠的;提出的颠覆性技术识别框架能充分利用专利数据信息和专利异质有向图中空间信息,识别出绝大部分的颠覆性技术,丰富了图神经网络在颠覆性技术识别方面的研究。

公安学科知识图谱构建与应用研究

为实现公安学科领域知识的结构化组织,以降低学习者认知负荷、促进鉴定等提高公安实战工作效率,采用自上而下的方式构建公安学科知识图谱。由公安应用需求驱动模式层本体设计,并提出数据层知识获取、融合和计算方法。基于OWL本体与图数据库存储模型间的映射规则,设计Neo4j数据库下的知识图谱存储方案。评估实验结果表明,图谱可支持各类检索操作,并返回正确结果。公安学科知识图谱能够以实战问题为核心充分表示领域知识,并满足支持公安实战和领域知识深度学习等应用需求。

基于知识图谱的个性化音乐推荐系统设计与实现

本文基于知识图谱技术设计了个性化音乐推荐系统。该系统由用户端、管理端、服务端以及数据库组成。用户完成注册并登录系统后,可以通过输入关键词等方式检索对应的歌曲和歌单,并将其添加收藏,管理员可查看并编辑用户信息。当用户再次登录该系统时,系统会根据之前用户的操作行为向用户精准推送个性化的歌曲和歌单,并在网站主页上显示。从测试情况来看,该系统的音乐推荐、歌曲搜索、歌曲收藏等基本功能可以实现,系统各项操作的平均响应时间均在1 s以内,使用体验较好。

呼吸系统疾病知识图谱的构建

呼吸系统疾病是中国十大死亡率疾病之一。为解决其数据分布庞杂、难以深层次应用的问题,通过采用自顶向下的方法,构建了呼吸系统疾病知识图谱。首先,参考医学文献构建了呼吸疾病词典,并根据该疾病词典爬取了垂直医疗网站的呼吸疾病数据;然后,利用知识融合完成多源异构数据的链接,通过定义不同疾病属性相似度来计算疾病实体相似度,提高准确率;最后,将呼吸系统疾病数据存储到Neo4j中,并做可视化展示。

基于本体库的知识抽取及图谱构建技术

该文首先介绍领域内本体库的组成架构,以及基础数据分析和WordNet节选的主要功能。随后,提出一种基于本体库的实体数据抽取技术,在不同的实体之间建立语义关系,为知识抽取做好铺垫。在实体信息抽取过程中,首先要判断网页是否在领域内,在确定网页属于领域后按照特定的标签划分网页内容,进而抽取出有价值的实体数据。将抽取到的实体数据存储到Neo4j数据库中,定期更新知识图谱内的数据。当需要调用数据时,可以从知识图谱中检索需要的数据,从而实现数据资源的整合利用,发挥数据的价值。

中医古籍《灵枢》的知识图谱构建与可视化探讨

【目的】探讨《灵枢》的知识图谱构建与可视化方法,以期为中医古籍理论知识的结构化存储与展示提供思路。【方法】借鉴专业领域知识图谱的构建思路,利用文本挖掘技术,进行词库构建,对《灵枢》原文进行分词、实体识别、关系抽取等处理,得到知识图谱的构建基础。再利用图数据库Neo4j进行知识图谱的存储与查询,实现知识图谱的可视化展示。【结果】得到《灵枢》词库的1216个高质量词汇,实现了《灵枢》理论的知识图谱构建。构建的知识图谱可基本展示《灵枢》文本中描述的中医理论,如藏象与精气之间的关系、情志与五脏的关系等,使得相关实体与关系的检索调用成为可能,为中医古籍理论知识的结构化存储与展示提供了思路。【结论】通过知识图谱构建可以得到《灵枢》的中医理论知识图谱,展示《灵枢》中表达的阴阳五行、脏腑经络等的知识联系。知识图谱的构建与在图数据库中的存储,可使《灵枢》文本涉及的知识图谱能以可视化语义网络图的形式进行展示,还可使嵌入语义搜索、语义维基等系统成为可能,为中医智能医疗服务发展助力。

基于知识图谱与SCD文件的智能变电站二次检修安全措施自动生成技术研究

智能站采用光纤传递数字和模拟信号,使常规站基于“短电流、断电压、拆跳闸”方式的二次安全措施理论不再适用于智能站。为解决传统人工拟票方式编制效率低、错误率高等问题,提出基于知识图谱与智能变电站配置描述文件(substation configuration description, SCD)的二次检修安全措施自动生成技术。首先,基于SCD文件和知识图谱构建配置数据库,并按照设定规则实现虚回路与软压板的半自动关联。其次,依据智能站规范中的二次安措编制准则,构建安措规则库并引入安措执行优先级编码字段。最后,由二次安措自动生成技术基于配置数据库与安措规则库完成检修设备二次信息的规则匹配,用于自动生成二次安措票。仿真结果表明,该技术能够根据现场检修任务自动正确生成二次安措票,提高智能变电站技改、消缺和校验的工作效率,降低人工编制错误率,为实现智能化运检一体提供参考。

高校专利转移分析平台的构建与实证研究

[目的/意义]构建针对高校专利转移数据的知识图谱体系,并完成具有集成功能的分析平台开发,为高校专利成果推广和运营策略的制定与实施提供参考依据。[方法/过程]通过分析高校专利转移数据及其内在语义关系,基于本体工程构建高校专利转移知识图谱的属性图数据模型,结合Python应用程序设计完成数据预处理和知识抽取,使用Neo4j图数据库存储图谱信息,采用C/S架构使用JavaFX对高校专利转移分析平台进行设计与开发。[结果/结论]平台通过人机交互界面,集合项目管理、知识图谱构建与维护、统计分析、用户管理等功能,便于专利成果转移关联数据的查询与分析,为专利管理及运营人员提供了研究专利转让趋势与特征的新工具。

基于动态图注意力与标签传播的实体对齐

实体对齐是多源数据库融合的有效方法,旨在找出多源知识图谱中的共指实体。近年来,图卷积网络(GCN)已成为实体对齐表示学习的新范式,然而,不同组织构建知识图谱的目标及规则存在巨大差异,要求实体对齐模型能够准确发掘知识图谱之间的长尾实体特征,并且现有的GCN实体对齐模型过于注重关系三元组的结构表示学习,忽略了属性三元组丰富的语义信息。为此,提出一种实体对齐模型,引入动态图注意力网络聚合属性结构三元组表示,降低无关属性结构对实体表示的影响。同时,为缓解知识图谱的关系异构问题,引入多维标签传播对实体邻接矩阵的不同维度进行压缩,将实体特征根据压缩后的知识图谱邻接关系进行传播以获得关系结构表示,最后通过线性规划算法对实体表示相似度矩阵进行迭代以得到最终的对齐结果。在公开数据集ENFR-15K、EN-ZH-15K以及中文医学数据集MED-BBK-9K上进行实验,结果表明,该模型的Hits@1分别为0.942、0.926、0.427,Hits@10分别为0.963、0.952、0.604,MRR分别为0.949、0.939、0.551,消融实验结果也验证了模型中各模块的有效性。

基于模型的机床故障案例命名实体抽取方法比较研究

机床出现的故障大多有先例,但故障案例分散,不同工厂又不数据共享且没有标准的数据库管理,以至于对于已有的相似故障,工厂仍需要按照未知故障进行停机维修;因此,急需一套标准服务平台能够集合大量故障案例,同时实现更新维护,增添新故障,以供各工厂做故障参考,尽可能降低维修成本以及时间开销;通过将计算机领域较为流行的知识图谱运用到机床故障诊断领域,全面运用机床故障诊断案例知识,构建以故障现象、故障发生原因以及解决方案为核心的机床故障诊断网络,实现快速确认故障发生部位,提供合理的故障解决方案,提高制造业的生产效率;使用爬虫技术获取故障案例数据,采用BIO标注法完成样本标注,分别使用Bilstrm-crf、Vgg16以及Bert模型完成实体抽取任务,并对上述模型准确率从多个角度进行对比,将知识导入Neo4J图数据库并建立针对机床故障的知识图谱,最终实现知识图谱可视化。

面向建筑施工安全的标准知识支持研究

为在建筑施工过程中高效获取安全标准知识,通过人工与自然语言处理技术,从建筑施工安全标准中抽取安全知识,在此基础上赋予编码构建出建筑施工安全标准知识图谱。提出基于知识图谱的3个建筑施工安全标准知识分析指标。通过试运行验证建筑施工安全标准知识图谱在结构化表达、抽取和调用相关知识方面具有良好的可用性。研究结果表明:构建的建筑施工安全标准知识图谱可以有效地将标准知识进行结构化表达,对建筑安全标准知识进行分析可进一步挖掘知识间关联。研究结果可为建筑施工提供准确、全面、及时的安全标准知识支持,提升建筑从业人员的安全知识水平,减少安全事故发生。