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

Global Energy Interconnection 2023年第1期6卷 50-63页

作者： Daoxing Li Kai Xiao Xiaohui Wang Pengtian Guo Yong Chen China Electric Power Research Institute Co.Ltd. Beijing 100192P.R.China

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.

关键词： Graph database graph description Matrix Parallel computing Power flow

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Query Optimization Framework for Graph database in Cloud Dew Environment

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Computers, Materials & Continua 2023年第1期74卷 2317-2330页

作者： Tahir Alyas Ali Alzahrani Yazed Alsaawy Khalid Alissa Qaiser Abbas Nadia Tabassum Department of Computer Science Lahore Garrison UniversityLahorePakistan Faculty of Computer Science and Information Systems Islamic University Madinah Madinah42351Saudi Arabia Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Virtual University of PakistanLahorePakistan

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.

关键词： Query optimization compression cloud dew decompression Graph database

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

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Science China(Technological Sciences) 2019年第12期62卷 2088-2092页

作者： CHEN Jian JIANG Ying LU WenDa HAN Meng LI XiaoMing State Grid Zhejiang Electric Power Co Ltd.Hangzhou 310007China School of Computer Science and Engineering Nanjing University of Science&TechnologyNanjing 210094China Beijing Zhongdian Puhua Information Technology Co.LTD Beijing 100192China

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.

关键词： power communication network Graph database fault-spreading algorithm predicate calculus

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Processes and events in the centre:a dynamic data model for representing spatial change

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International Journal of Digital Earth 2022年第1期15卷 276-295页

作者： Yufeng He Yehua Sheng Barbara Hofer Yi Huang Jiarui Qin School of Geography Nanjing Normal UniversityNanjingPeople’s Republic of China Key Laboratory of Virtual Geographic Environment (Nanjing Normal University) Ministry of EducationNanjingPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China Department of Geoinformatics-Z_GIS University of SalzburgSalzburgAustria School of Geographic and Biologic Information Nanjing University of Posts and TelecommunicationsNanjingPeople’s Republic of China

Traditional geographic information system models for map representation use superposition of layers to model physical reality,neglecting the integrity of the environment and limiting the ability to express interactions between features in complex phenomenon.This results in limitations regarding dynamic simulation and geographic causality reasoning.In this paper,we extend the framework of the geographic scene by formalizing the relationship between geographic processes and events to construct a dynamic data model:the process-event-centred dynamic data model.The key element of this data model is relationships between processes,events,and states of the natural or man-made phenomenon of interest.The identified relationships can be translated into a network of hierarchical,developmental,and causal graphs and realized in the Neo4j Graph database.The implementation in the Graph database supports spatio-temporal reasoning in geographic scenes and achieves an organizational framework for simulating spatio-temporal dynamics and complex calculations.The example of a 2019 mega-typhoon process is used to demonstrate the introduced process-event-centred model and its implementation in the Graph database.A series of queries to the Graph database show the capabilities of the data model for spatial reasoning and dynamic modeling.

关键词： Spatio-temporal dynamics geographic scene geographic processes geographic events spatio-temporal reasoning Graph database

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Building a High-Performance graph Storage on Top of Tree-Structured Key-Value Stores

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Big Data Mining and Analytics 2024年第1期7卷 156-170页

作者： Heng Lin Zhiyong Wang Shipeng Qi Xiaowei Zhu Chuntao Hong Wenguang Chen Yingwei Luo School of Computer Science Peking UniversityBeijing 100871China Ant Group Beijing 100020China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications,including financial risk assessment,commodity recommendation,and data lineage tracking.While the principles and design of these databases have been the subject of some investigation,there remains a lack of comprehensive examination of aspects such as storage layout,query language,and deployment.The present study focuses on the design and implementation of graph storage layout,with a particular emphasis on tree-structured key-value stores.We also examine different design choices in the graph storage layer and present our findings through the development of Tugraph,a highly efficient single-machine Graph database that significantly outperforms well-known Graph database Management System(GDBMS).Additionally,Tugraph demonstrates superior performance in the Linked Data Benchmark Council(LDBC)Social Network Benchmark(SNB)interactive benchmark.

关键词： Graph database high-performance graph storage

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Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 165-182页

作者： Kiran Fahd Sitalakshmi Venkatraman Department of Information Technology Melbourne Polytechnic3181 PrahranVICAustralia

Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related articles.Despite the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is warranted.The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic visualization.To address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories.Contemporary topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the literature.The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles.This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk factors.The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles.It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles.Further,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.In this study,the implementation of the proposed

关键词： Big data Data visualization Knowledge maps Dementia Non-relational database Graph database Neo4j Semantic visualization

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Redesign of the gStore system

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Frontiers of Computer Science 2018年第4期12卷 623-641页

作者： Li ZENG Lei ZOU School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Beijing Institute of Big Data Research Beijing 100871 China

gStore is an open-source native Resource Description Framework （RDF） triple store that answers SPARQL queries by subgraph matching over RDF graphs. However, there are some deficiencies in the original system design, such as answering simple queries （including one-triple pattern queries）. To improve the efficiency of the system, we reconsider the system design in this paper. Specifically, we propose a new query plan generation module that generates different query plans according to the structures of query graphs. Furthermore, we re-design our vertex encoding strategy to achieve more pruning power and a new multi-join algorithm to speed up the subgraph matching process. Extensive experiments on synthetic and real RDF datasets show that our method outperforms the state-of-the-art algorithms significantly.

关键词： Graph database subgraph matching RDF management SPARQL query

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The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network

The Performance Evaluation and Resilience Analysis of Supply...

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第三十九届中国控制会议

作者： Weiqi Sun Yuanlong Li Liangren Shi Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical Engineering Shanghai Jiao Tong University

With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4 j Graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.

关键词： Complex network Supply chain network Graph database Neo4j Resilience evaluation

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一门交叉学科的兴起：论图数据科学的构建

图书馆学情报学

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图书馆学情报学 2023年第8期

作者：周贞云邱均平

图无处不在，数据无时不有，科学无所不至，图数据科学的学科构建迫在眉睫。文章追溯图数据科学的产生与发展，从理论、方法和应用视角探究图数据科学的内容体系，在此基础上阐明图数据科学的发展趋势。研究发现：从图论到图建模、从关... 详细信息

图无处不在，数据无时不有，科学无所不至，图数据科学的学科构建迫在眉睫。文章追溯图数据科学的产生与发展，从理论、方法和应用视角探究图数据科学的内容体系，在此基础上阐明图数据科学的发展趋势。研究发现：从图论到图建模、从关系数据库到图数据库、从大数据到图数据、从图到知识图谱、从行业应用到技术融合的历程，见证了图数据科学的学科兴起与发展；图数据科学是探索图数据及其现实世界的现象和规律的新兴学科，是主要由数学、计算机科学、人工智能融合而成的一门交叉学科；图数据科学荟萃了图数据库、知识图谱、大图分析为核心的学科内容，在理论、方法和应用上形成了崭新的学科体系；图数据科学具有融合化、数智化、工程化的发展趋势，成为一门独立性与系统性、创新性与驱动性、工程性与管理性相统一的综合学科。

关键词：图数据科学交叉学科学科构建知识图谱图数据库　　graph data science interdisciplinary disciplinary construction knowledge graph Graph database

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