A Cypher query based NoSQL data mining on protein datasets using Neo4j graph database

Johnpaul, C I; Mathew, Tojo

doi:10.1109/icaccs.2017.8014558

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…A graph structure is used to represent semantic queries with nodes, relationships and properties, and store data. Due to its ability to present data in a robust and scalable way, we use the Neo4j graph database management system so that queries with multiple relationships can be identified [13,34]. To automatically identify hazards, we perform the following tasks: (1) data modelling; and (2) automated reasoning and query.…”

Section: Knowledge Inference For Hazard Identification With Graph Databasementioning

confidence: 99%

Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology

Fang

Love

et al. 2020

Automation in Construction

134

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Hazards potentially affect the safety of people on construction sites include falls from heights (FFH), trench and scaffold collapse, electric shock and arc flash/arc blast, and failure to use proper personal protective equipment. Such hazards are significant contributors to accidents and fatalities. Computer vision has been used to automatically detect safety hazards to assist with the mitigation of accidents and fatalities. However, as safety regulations are subject to change and become more stringent prevailing computer vision approaches will become obsolete as they are unable to accommodate the adjustments that are made to practice. This paper integrates computer vision algorithms with ontology models to develop a knowledge graph that can automatically and accurately recognise hazards while adhering to safety regulations, even when they are subjected to change. Our developed knowledge graph consists of: (1) an ontological model for hazards: (2) knowledge extraction; and (3) knowledge inference for hazard identification. We focus on the detection of hazards associated with FFH as an example to illustrate our proposed approach. We also demonstrate that our approach can successfully detect FFH hazards in varying contexts from images.

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Section: Knowledge Inference For Hazard Identification With Graph Databasementioning

confidence: 99%

Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology

Fang

Love

et al. 2020

Automation in Construction

134

View full text Add to dashboard Cite

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“…Database Neo4j adalah sebuah database grafik open source berbasis java dengan kinerja yang tinggi [10]. Noe4j salah satu graph database yang dapat memvisualisasikan node-node yang saling terhubung yang popular dalam era komputasi saat ini [11]. Neo4j menyediakan cara mudah untuk memvisualisasikan data dengan struktur grafik yang baik.…”

Section: Graph Database Neo4junclassified

Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

Sahria

Fudholi

2020

mib

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Health research is research approved in the medical field. Health research in Indonesia is increasingly being approved by researchers in Indonesia, therefore research is stored in repositories. Many health research journal repositories are available, but there is minimal research for analysis and modeling in the health research network in Indonesia. This research proposes a knowledge modeling with Neo4j graphic database to implement it. In this study, data obtained from the SINTA Journal with web scraping techniques. The purpose of this research is to produce network knowledge using CQL (Chyper Query Language) which is useful in the medical field. The results of this study are expected to be useful for the government, academics, researchers, or the public for health research network researchers in Indonesia. The results of research testing modeling of health research network knowledge using a graph database of 95.2% said very good

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“…Neo4j can support the operations of storage, query, backup, and redundancy for large-scale data, and it also has the properties of atomicity, consistency, isolation, and durability (ACID) [24]. At the same time, it also supports the query language Cypher, which is an expressive and efficient declarative graph database query language [25]. Besides, Cypher is so scalable that the users can customize their own query methods conveniently.…”

Section: Neo4j Graph Databasementioning

confidence: 99%

Graph database-based network security situation awareness data storage method

Tao

Yang

Zhao

et al. 2018

J Wireless Com Network

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With the rapid development of the Internet, network security situation awareness has attracted tremendous attention. In large-scale complex networks, network security situation awareness data presents the characteristics of large-scale, multi-source, and heterogeneous. Recently, much research work have been done on network security situation awareness. However, most of the existing methods store different types of data in different ways, which makes data query and analysis inefficient. To solve this problem, we propose a graph database-based hierarchical multi-domain network security situation awareness data storage method. In our scheme, we build a hierarchical multi-domain network security situation awareness model to divide the network into different domains, which can collect and dispose the awareness data more efficiently. Meanwhile, to unify our storage mode, we also define network security situation awareness data storage rules and methods based on graph database. Finally, extensive experiments on real datasets show that our proposed method is efficient compared to state-of-the-art storage models.

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A Cypher query based NoSQL data mining on protein datasets using Neo4j graph database

Cited by 5 publications

References 9 publications

Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology

Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology

Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia

Graph database-based network security situation awareness data storage method

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