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.

Spanning tree-based algorithm for hydraulic simulation of large-scale water supply networks

With the purpose of making calculation more efficient in practical hydraulic simulations, an improved algorithm was proposed and was applied in the practical water distribution field. This methodology was developed by expanding the traditional loop-equation theory through utilization of the advantages of the graph theory in efficiency. The utilization of the spanning tree technique from graph theory makes the proposed algorithm efficient in calculation and simple to use for computer coding. The algorithms for topological generation and practical implementations are presented in detail in this paper. Through the application to a practical urban system, the consumption of the CPU time and computation memory were decreased while the accuracy was greatly enhanced compared with the present existing methods.

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.

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.

Modified Shifting Bottleneck Heuristic for Scheduling Problems of Large-Scale Job Shops

A modified shifting bottleneck algorithm was proposed to solve scheduling problems of a large-scale job shop.Firstly,a new structured algorithm was employed for sub-problems so as to reduce the computational burden and suit for large-scale instances more effectively.The modified cycle avoidance method,incorporating with the disjunctive graph model and topological sort algorithm,was applied to guaranteeing the feasibility of solutions with considering delayed precedence constraints.Finally,simulation experiments were carried out to verify the feasibility and effectiveness of the modified method.The results demonstrate that the proposed algorithm can solve the large-scale job shop scheduling problems(JSSPs) within a reasonable period of time and obtaining satisfactory solutions simultaneously.

Accurate querying of frequent subgraphs in power grid graph data

With the development of information technology, the amount of power grid topology data has gradually increased. Therefore, accurate querying of this data has become particularly important. Several researchers have chosen different indexing methods in the filtering stage to obtain more optimized query results because currently there is no uniform and efficient indexing mechanism that achieves good query results. In the traditional algorithm, the hash table for index storage is prone to "collision" problems, which decrease the index construction efficiency. Aiming at the problem of quick index entry, based on the construction of frequent subgraph indexes, a method of serialized storage optimization based on multiple hash tables is proposed. This method mainly uses the exploration sequence to make the keywords evenly distributed; it avoids conflicts of the stored procedure and performs a quick search of the index. The proposed algorithm mainly adopts the "filterverify" mechanism; in the filtering stage, the index is first established offline, and then the frequent subgraphs are found using the "contains logic" rule to obtain the candidate set. Experimental results show that this method can reduce the time and scale of candidate set generation and improve query efficiency.

基于RedisGraph的电网CIM/XML数据模型构建与应用

电网拓扑分析在电力系统运行规划中应用广泛。现有研究中,电网拓扑信息多以关系型数据库或图数据库的方式存储于磁盘,在查询速度上受限于数据库本身的特性。为了提高电网拓扑信息的查询速度,文章提出了一种基于内存型图数据库RedisGraph的电网拓扑模型构建与查询方法。首先概述了CIM/XML文件的数据结构和RedisGraph存储数据的方式,然后据此设计了将CIM/XML数据模型转换为电网拓扑图数据模型的方案,并实现了基于RedisGraph的拓扑搜索,最后基于构建的图数据模型进行拓扑搜索测试。结果表明,提出的基于RedisGraph的电网拓扑构建与查询方案能更好地满足大规模复杂电网的拓扑分析业务的性能需求。

Construction and application of knowledge graph of Treatise on Febrile Diseases

Objective To establish the knowledge graph of“disease-syndrome-symptom-method-formula”in Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)for reducing the fuzziness and uncertainty of data,and for laying a foundation for later knowledge reasoning and its application.Methods Under the guidance of experts in the classical formula of traditional Chinese medicine(TCM),the method of“top-down as the main,bottom-up as the auxiliary”was adopted to carry out knowledge extraction,knowledge fusion,and knowledge storage from the five aspects of the disease,syndrome,symptom,method,and formula for the original text of Treatise on Febrile Diseases,and so the knowledge graph of Treatise on Febrile Diseases was constructed.On this basis,the knowledge structure query and the knowledge relevance query were realized in a visual manner.Results The knowledge graph of“disease-syndrome-symptom-method-formula”in the Treatise on Febrile Diseases was constructed,containing 6469 entities and 10911 relational triples,on which the query of entities and their relationships can be carried out and the query result can be visualized.Conclusion The knowledge graph of Treatise on Febrile Diseases systematically realizes its digitization of the knowledge system,and improves the completeness and accuracy of the knowledge representation,and the connection between“disease-syndrome-symptom-treatment-formula”,which is conducive to the sharing and reuse of knowledge can be obtained in a clear and efficient way.

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.

A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries,expressed using Neo4j Cypher,to provide insights from the stored data for decision support.As proof of concept,a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.

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。

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。

基于鸟类迁徙关联要素数据的知识图谱构建

为解决当前鸟类迁徙关联要素数据量繁杂,传统方法不足以高效处理这些数据并精准分析关联要素之间的关系等问题,提出一种鸟类迁徙领域知识图谱构建方法。通过构建本体,利用黄河三角洲生态保护和高质量发展研究院提供的鸟类迁徙数据以及互联网大量文本信息来构建鸟类迁徙实体语料库,设计一种基于RoBERTa-BiLSTM-CRF的鸟类迁徙关联要素实体识别方法进行知识的抽取,利用文本相似度技术进行知识融合,将数据存入图数据库Neo4j中。实验结果表明,所提方法简单高效,构建的知识图谱扩充了鸟类迁徙领域的关联要素信息,是知识图谱技术在生态保护领域的应用与探索。

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。

基于科技文献的锡铟材料知识图谱构建研究

随着科技文献数量的爆发性增长,垂直领域的企业面临着知识服务的挑战。为了帮助新材料领域企业有效利用科技文献信息资源,迫切需要使用人工智能技术对包含的材料性能等关键知识的专利及学术论文进行深入的知识建模,提供知识获取效率和准确性。以锡铟贵金属领域的10.48万篇科技文献为数据源,构建领域知识本体,利用BERT+BiLSTM+CRF模型进行命名实体识别,利用BERT+BiGRU神经网络模型进行关系抽取,将抽取结果存入图数据库Neo4j中并构建锡铟贵金属材料领域的知识图谱。所构建的知识图谱拥有18.19万个实体节点和23.47万条关系,能够实现多粒度的材料实体和关系的关联查询与可视化。基于锡铟科技文献构建知识图谱的方法拓展了新材料领域知识图谱构建的研究思路,为开展基于科技文献的垂直领域智能知识问答等应用提供了坚实的基础。

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。

基于领域本体的数字孪生流域知识图谱构建与应用

为支撑数字孪生流域知识图谱的快速高效构建,以澧水流域为例,参照水利对象分类总则,通过梳理澧水流域内与江垭皂市工程相关的知识对象,选取关键属性,运用本体语义关系表达物理流域中的江河湖泊、水利工程和水利对象治理管理活动等实体、概念及其关系,构建了面向防洪兴利的数字孪生流域知识本体。基于水利领域本体模型和设计图数据模式进行结构化分类和关联,快速构建了数字孪生流域知识图谱;再利用MySQL和Neo4j图数据库进行知识混合存储,利用Cypher检索语句查询和应用对象关系,以支撑水利知识的快速检索和定位。