A novel approach based on Neo4j for multi-constrained flexible job shop scheduling problem

Zhu, Zhenwei; Zhou, Xionghui; Shao, Kang

doi:10.1016/j.cie.2019.03.022

Cited by 38 publications

(12 citation statements)

References 29 publications

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“…e model construction process can be generally divided into data collection, labeling, analysis, and application phases. e whole construction process is semi-automated with the help of Neo4j [20]. e proposed knowledge graph construction model has the following contributions to both fields of computer science and smart grid development.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Construction and Application of Power Grid Equipment

Haichao

Hong

Zhou

et al. 2020

Mathematical Problems in Engineering

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Recent development of artificial intelligence (AI) technology enquires the traditional power grid system involving additional information and connectivity of all devices for the smooth transit to the next generation of smart grid system. In an AI-enhanced power grid system, each device has its unique name, function, property, location, and many more. A large number of power grid devices can form a complex power grid knowledge graph through serial and parallel connection relationships. The scale of power grid equipment is usually extremely large, with thousands and millions of power devices. Finding the proper way of understanding and operating these devices is difficult. Furthermore, the collection, analysis, and management of power grid equipment become major problems in power grid management. With the development of AI technology, the combination of labeling technology and knowledge graph technology provides a new solution understanding the internal structure of a power grid. As a result, this study focuses on knowledge graph construction techniques for large scale power grid located in China. A semiautomatic knowledge graph construction technology is proposed and applied to the power grid equipment system. Through a series of experimental simulations, we show that the efficiency of daily operations, maintenance, and management of the power grid can be largely improved.

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Section: Introductionmentioning

confidence: 99%

Knowledge Graph Construction and Application of Power Grid Equipment

Haichao

Hong

Zhou

et al. 2020

Mathematical Problems in Engineering

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“…The nodes are described as the main element that connected to other nodes using relationship. The node and edges have properties that can be stored as key-value whereas label is described as roles to define types of node in the graph [35].…”

Section: Analysis Results and Discussionmentioning

confidence: 99%

Ransomware Behavior Attack Construction via Graph Theory Approach

Rosli¹,

Abdullah²,

Yassin³

et al. 2020

IJACSA

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Ransomware has becoming a current trend of cyberattack where its reputation among malware that cause a massive amount recovery in terms of cost and time for ransomware victims. Previous studies and solutions have showed that when it comes to malware detection, malware behavior need to be prioritized and analyzed in order to recognize malware attack pattern. Although the current state-of-art solutions and frameworks used dynamic analysis approach such as machine learning that provide more impact rather than static approach, but there is not any approachable way in representing the analysis especially a detection that relies on malware behavior. Therefore, this paper proposed a graph theory approach which is analysis of the ransomware behavior that can be visualized into graph-based pattern. An experiment has been conducted with ten ransomware samples for malware analysis and verified using VirusTotal. Then, file system among features were selected in the experiment as a medium to understand the behavior of ransomware using data capturing tools. After that, the result of the analysis was visualized in a graph pattern based on Neo4j which is graph database tool. By using graph as a base, the discussion has been made to recognize each type of ransomware that acts differently in the file system and analyze which node that have the most impact during analysis part.

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“…So it is easier to express real world business scenarios. Typically, open source graph databases are Neo4j [8], Arangodb [9], etc. Since the database itself has provided a perfect graph query language and supports various graph mining algorithms, it has efficient graph query and search functions.…”

Section: Knowledge Graph Storagementioning

confidence: 99%

Distributed Storage and Query for Domain Knowledge Graphs

Shan

Shi

et al. 2021

Communications in Computer and Information Science

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The development of knowledge graph needs the support of a vast quantity of data. However, the amount of data increases rapidly is placing increasing demands on machines. Centralized data storage requires high-performance hosts to store data, which is costly and have single point of failure. Distributed data storage can reduce the cost of the machine greatly, and there is no single point of failure, but it has requirements for partition and storage of data collection. In the knowledge storage of specific domain, the way of graph data partition and storage vary from the different domain knowledge. To solve the above problems, a scheme of graph partition and distributed storage for domain-specific knowledge graphs is proposed. The proposed graph partition scheme pays attention to the correlation between the data, and divides the nodes affiliated each other into the same or similar partition. A distributed aggregation storage scheme is designed, which makes full use of cluster performance and solves the problem of data consistency during data insertion and update. The proposed distributed storage scheme based on HBase combines Neo4j to realize visual query effectively. Experimental results show the efficiency and the effectiveness of the proposed method in partition time, the number of edge-cut and update time.

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A novel approach based on Neo4j for multi-constrained flexible job shop scheduling problem

Cited by 38 publications

References 29 publications

Knowledge Graph Construction and Application of Power Grid Equipment

Knowledge Graph Construction and Application of Power Grid Equipment

Ransomware Behavior Attack Construction via Graph Theory Approach

Distributed Storage and Query for Domain Knowledge Graphs

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