Graph database
Wood, Peter T. · BIROn (Birkbeck, University of London) · 2009
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Graph Database
Dovgin, George · OPUS - Open Portal to University Scholarship (Governors State University) · 2015
This project will review the new technology of graph databases. Graph databases, which model data using nodes and relationships, utilize a different paradigm than the rows and columns of relational databases.\nThe main goals of this project are to provide the basic background information on graph database technology and then use this knowledge to convert an RDBMS into a GDBMS. The RDBMS used will be the sample Accounts Payable (AP) relational database used in the Murach SQL 2012 book. The following will be accomplished: Explore graph database versus relational for querying and updating the Accounts Payable database. Review Cypher (Neo4j graph query language) and run CRUD queries against the AP graph database. Show step by step instructions to convert the Murach SQL 2012 Accounts Payable database into the graph database.
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Graph Database
Dovgin, George · OPUS - Open Portal to University Scholarship (Governors State University) · 2015
This project will review the new technology of graph databases. Graph databases, which model data using nodes and relationships, utilize a different paradigm than the rows and columns of relational databases. The main goals of this project are to provide the basic background information on graph database technology and then use this knowledge to convert an RDBMS into a GDBMS. The RDBMS used will be the sample Accounts Payable (AP) relational database used in the Murach SQL 2012 book. The following will be accomplished: Explore graph database versus relational for querying and updating the Accounts Payable database. Review Cypher (Neo4j graph query language) and run CRUD queries against the AP graph database. Show step by step instructions to convert the Murach SQL 2012 Accounts Payable database into the graph database.
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Graph Database
Renchang Dai, Guangyi Liu · 2023
The chapter demonstrates the process of defining a graph schema, creating a loading job and executing various graph queries such as data retrieval, sorting, filtering, deletion, insertion, updating, and traversal to construct and manipulate data in a graph database. Graph partitioning and parallel processing play crucial roles in the graph processing engine of a graph database management system, enabling efficient parallel computing. Built-in functions for local and global graph partitions ensure optimal partitioning, balancing subgraph sizes, and minimizing communication between subgraphs. The chapter also outlines the map and reduce functions used in parallel processing, illustrating their implementation within the MapReduce mechanism.
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Attack Graph to Graph Database
Saurav Saraf · IOSR Journal of Computer Engineering · 2014
Databases are an integral part of almost any computing system today, and users heavily rely on the services they provide. When we interact with a computing system, we expect that any data be stored for future use, that the data is able to be looked up quickly, and that we can perform complex queries against the data stored in the database. There are many different emerging database types available for use, such as relational databases, key-value databases, object databases, graph databases, and RDF databases. Each type of database provides a unique set of qualities that have applications in various domains. Our work aims to investigate and compare the performance of relational databases to graph databases in terms of handling Attack Graph data. In this following project work, we transform the Attack graph data (stored in the xml format) to a Graph Database format using Neo4j. This converted format can be viewed using the Neo4j service & local host in the web browser. The further work of the project has also been attached.
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Attack Graph to Graph Database
Iosr Journals, Saurav Saraff · Figshare · 2014
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Knowldge Graph Database
Enya Nieland · Figshare · 2019
This database contains metadata of several Knowledge Graphs, such as the amount of nodes, the indegree and the outdegree.
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Graph Database Mining
SpringerReference · 2011
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Graph Database Fundamentals
Vedat Bobby Batatina · 2026
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CIS Graph Database
Stanislava Gardasevic · Figshare · 2022
This dataset is based on the model developed together with the PhD students of Communication and Information Sciences PhD program at University of Hawaii at Manoa, intended to help new students get relevant information. The model was presented at the iConference 2023, in a paper "Community Design of a Knowledge Graph to Support Interdisciplinary Ph.D. Students ". It contains publically available data collected from multiple web locations and indexes of the sample of publications from the people in this domain. Except for that, it contains my (first author's) personal graph that demonstrates progress through the program of a student in this degree. <br> It is intended to provide researchers with an opportunity to investigate multigraph visualization of the data modeled by the said model. <br> The database is created in Neo4J and here you can download the .dump file to import to this software. <br> <br> <br>
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NeoDB Graph Database
Thoba Lose, Peter Van Heusden · Figshare · 2019
NeoDB is a free and open source integrated M.tuberculosis ‘omics’ knowledge-base. NeoDB is based on Neo4j and enables researchers to execute complex federated queries by linking well-known, curated and widely used biological data resources, and supplementary TB variants data from published literature.
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Knowldge Graph Database
Enya Nieland · Figshare · 2019
This database contains metadata of several Knowledge Graphs, such as the amount of nodes, the indegree and the outdegree.
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Luigi Usai Graph Database
Usai, Luigi · Zenodo (CERN European Organization for Nuclear Research) · 2025
Italiano 1. LUG-DB (Luigi Usai Graph Database) Titolo: LUG-DB: Un Visualizzatore Interattivo di Database a Grafo Descrizione: LUG-DB (Luigi Usai Graph Database) è un'applicazione desktop leggera, sviluppata in Python con Tkinter, progettata per l'importazione, la visualizzazione e l'esplorazione interattiva di dati a grafo. Lo strumento è ideale per studenti, sviluppatori e ricercatori che necessitano di un modo rapido per ispezionare set di dati strutturati a grafo senza la complessità di installare e configurare un sistema di database completo. Caratteristiche Principali: Motore a Grafo In-Memory: Implementa un semplice database a grafo di proprietà (property graph) in memoria, rendendo l'applicazione autonoma e facile da avviare. Importazione Flessibile: Supporta l'importazione di dati da molteplici formati: JSON: Include una logica di parsing intelligente in grado di interpretare strutture annidate per creare automaticamente nodi e relazioni gerarchiche. RDF/Turtle: Utilizza la libreria rdflib per parsare formati standard del Web Semantico come RDF/XML e Turtle (TTL), trasformando triple in nodi, proprietà e relazioni. Visualizzazione su Canvas: Rappresenta i grafi in modo intuitivo, disegnando nodi (come cerchi colorati in base alle loro etichette) e relazioni (come frecce etichettate). Manipolazione Diretta: Gli utenti possono trascinare e riposizionare i nodi direttamente sull'area di disegno per migliorare la leggibilità del grafo. Interfaccia Grafica Semplice: Dotato di un menu intuitivo per l'importazione dei file, la visualizzazione dei log di operazioni e l'accesso alle informazioni sul software. Tecnologie: Python, Tkinter, rdflib. 2. GraphRAG Silent Expander Titolo: GraphRAG Silent Expander: Un Crawler per la Costruzione Automatica di Knowledge Graph da Fonti Web Descrizione: GraphRAG Silent Expander è un'applicazione Python che automatizza il processo di crawling del web per estrarre entità nominate (persone, organizzazioni, luoghi) e le loro relazioni contestuali, costruendo dinamicamente un knowledge graph. Lo strumento è stato progettato per ricercatori e sviluppatori che lavorano nel campo dell'Information Extraction, del Semantic Web e della preparazione di dati per sistemi di Retrieval-Augmented Generation (RAG), fornendo un metodo per trasformare il web non strutturato in una knowledge base strutturata e interoperabile. Metodologia e Caratteristiche: Crawling Web Intelligente: Utilizza Selenium per navigare siti web in modo automatizzato. Gestisce una coda di URL in modo "educato" (polite crawling), alternando i domini e introducendo ritardi per evitare di sovraccaricare i server. Estrazione di Contenuto: Si affida alla libreria trafilatura per isolare e pulire il contenuto testuale principale delle pagine, scartando elementi superflui come menu, pubblicità e footer. Riconoscimento di Entità (NER): Analizza il testo estratto con il modello linguistico italiano di spaCy per identificare e classificare le entità. Costruzione del Grafo: Le entità identificate diventano i nodi di un grafo gestito con NetworkX. Le relazioni (archi) vengono create tra entità che co-occorrono all'interno della stessa frase, rappresentando un forte legame contestuale. Esportazione Semantica in TTL: La funzionalità chiave è la capacità di esportare il grafo in file Turtle (TTL). Per ogni URL sorgente, viene generato un file TTL separato contenente solo i nodi e le relazioni estratte da quella pagina, con le entità mappate a tipi RDF standard (es. foaf:Person, geo:Place). Gestione delle Sessioni: Salva automaticamente lo stato del grafo, la coda degli URL e l'elenco dei siti visitati, permettendo di interrompere e riprendere sessioni di crawling lunghe. Interfaccia di Monitoraggio: Una GUI basata su Tkinter fornisce controlli per avviare/fermare il processo e visualizza statistiche in tempo reale (numero di nodi/archi, URL in coda) e un log dettagliato delle operazioni. Tecnologie: Python, Tkinter, Selenium, spaCy, NetworkX, trafilatura. English 1. LUG-DB (Luigi Usai Graph Database) Title: LUG-DB: An Interactive Graph Database Viewer Description: LUG-DB (Luigi Usai Graph Database) is a lightweight desktop application, developed in Python with Tkinter, designed for the import, visualization, and interactive exploration of graph data. The tool is ideal for students, developers, and researchers who need a quick way to inspect graph-structured datasets without the overhead of installing and configuring a full-fledged database system. Key Features: In-Memory Graph Engine: Implements a simple in-memory property graph database, making the application self-contained and easy to run. Flexible Data Import: Supports data import from multiple formats: JSON: Includes an intelligent parsing logic capable of interpreting nested structures to automatically create nodes and hierarchical relationships. RDF/Turtle: Uses the rdflib library to parse standard Semantic Web formats like RDF/XML and Turtle (TTL), transforming triples into nodes, properties, and relationships. Canvas-Based Visualization: Intuitively represents graphs by drawing nodes (as circles colored according to their labels) and relationships (as labeled arrows). Direct Manipulation: Users can drag and reposition nodes directly on the canvas to improve the graph's readability. Simple GUI: Features an intuitive menu for importing files, viewing operation logs, and accessing software information. Technologies: Python, Tkinter, rdflib. 2. GraphRAG Silent Expander Title: GraphRAG Silent Expander: A Crawler for Automatically Building Knowledge Graphs from Web Sources Description: GraphRAG Silent Expander is a Python application that automates the process of crawling the web to extract named entities (people, organizations, locations) and their contextual relationships, dynamically building a knowledge graph. This tool is designed for researchers and developers in the fields of Information Extraction, Semantic Web, and data preparation for Retrieval-Augmented Generation (RAG) systems, providing a method to transform the unstructured web into a structured and interoperable knowledge base. Methodology and Features: Intelligent Web Crawling: Uses Selenium to navigate websites automatically. It politely manages a URL queue by cycling through domains and introducing delays to avoid overloading servers. Content Extraction: Relies on the trafilatura library to isolate and clean the main textual content of pages, discarding superfluous elements like menus, ads, and footers. Named Entity Recognition (NER): Analyzes the extracted text using spaCy's Italian language model to identify and classify entities. Graph Construction: The identified entities become nodes in a graph managed with NetworkX. Relationships (edges) are created between entities that co-occur within the same sentence, representing a strong contextual link. Semantic TTL Export: Its key feature is the ability to export the graph into Turtle (TTL) files. For each source URL, a separate TTL file is generated containing only the nodes and relationships extracted from that page, with entities mapped to standard RDF types (e.g., foaf:Person, geo:Place). Session Management: Automatically saves the state of the graph, the URL queue, and the list of visited sites, allowing long crawling sessions to be paused and resumed. Monitoring Interface: A Tkinter-based GUI provides controls to start/stop the process and displays real-time statistics (node/edge counts, URLs in queue) and a detailed operation log. Technologies: Python, Tkinter, Selenium, spaCy, NetworkX, trafilatura.
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An Orthology Graph Database
Sarigol, Fatih · Zenodo (CERN European Organization for Nuclear Research) · 2024
More information about this dataset is available below: Topological mixing and irreversibility in animal chromosome evolution Darrin T. Schultz, Arno Blümel, Dalila Destanović, Fatih Sarigol, Oleg Simakov bioRxiv 2024.07.29.605683; doi: https://doi.org/10.1101/2024.07.29.605683 If you use this database in your study, please cite.
Cited by 0Open access
Luigi Usai Graph Database
Usai, Luigi · Zenodo (CERN European Organization for Nuclear Research) · 2025
Italiano 1. LUG-DB (Luigi Usai Graph Database) Titolo: LUG-DB: Un Visualizzatore Interattivo di Database a Grafo Descrizione: LUG-DB (Luigi Usai Graph Database) è un'applicazione desktop leggera, sviluppata in Python con Tkinter, progettata per l'importazione, la visualizzazione e l'esplorazione interattiva di dati a grafo. Lo strumento è ideale per studenti, sviluppatori e ricercatori che necessitano di un modo rapido per ispezionare set di dati strutturati a grafo senza la complessità di installare e configurare un sistema di database completo. Caratteristiche Principali: Motore a Grafo In-Memory: Implementa un semplice database a grafo di proprietà (property graph) in memoria, rendendo l'applicazione autonoma e facile da avviare. Importazione Flessibile: Supporta l'importazione di dati da molteplici formati: JSON: Include una logica di parsing intelligente in grado di interpretare strutture annidate per creare automaticamente nodi e relazioni gerarchiche. RDF/Turtle: Utilizza la libreria rdflib per parsare formati standard del Web Semantico come RDF/XML e Turtle (TTL), trasformando triple in nodi, proprietà e relazioni. Visualizzazione su Canvas: Rappresenta i grafi in modo intuitivo, disegnando nodi (come cerchi colorati in base alle loro etichette) e relazioni (come frecce etichettate). Manipolazione Diretta: Gli utenti possono trascinare e riposizionare i nodi direttamente sull'area di disegno per migliorare la leggibilità del grafo. Interfaccia Grafica Semplice: Dotato di un menu intuitivo per l'importazione dei file, la visualizzazione dei log di operazioni e l'accesso alle informazioni sul software. Tecnologie: Python, Tkinter, rdflib. 2. GraphRAG Silent Expander Titolo: GraphRAG Silent Expander: Un Crawler per la Costruzione Automatica di Knowledge Graph da Fonti Web Descrizione: GraphRAG Silent Expander è un'applicazione Python che automatizza il processo di crawling del web per estrarre entità nominate (persone, organizzazioni, luoghi) e le loro relazioni contestuali, costruendo dinamicamente un knowledge graph. Lo strumento è stato progettato per ricercatori e sviluppatori che lavorano nel campo dell'Information Extraction, del Semantic Web e della preparazione di dati per sistemi di Retrieval-Augmented Generation (RAG), fornendo un metodo per trasformare il web non strutturato in una knowledge base strutturata e interoperabile. Metodologia e Caratteristiche: Crawling Web Intelligente: Utilizza Selenium per navigare siti web in modo automatizzato. Gestisce una coda di URL in modo "educato" (polite crawling), alternando i domini e introducendo ritardi per evitare di sovraccaricare i server. Estrazione di Contenuto: Si affida alla libreria trafilatura per isolare e pulire il contenuto testuale principale delle pagine, scartando elementi superflui come menu, pubblicità e footer. Riconoscimento di Entità (NER): Analizza il testo estratto con il modello linguistico italiano di spaCy per identificare e classificare le entità. Costruzione del Grafo: Le entità identificate diventano i nodi di un grafo gestito con NetworkX. Le relazioni (archi) vengono create tra entità che co-occorrono all'interno della stessa frase, rappresentando un forte legame contestuale. Esportazione Semantica in TTL: La funzionalità chiave è la capacità di esportare il grafo in file Turtle (TTL). Per ogni URL sorgente, viene generato un file TTL separato contenente solo i nodi e le relazioni estratte da quella pagina, con le entità mappate a tipi RDF standard (es. foaf:Person, geo:Place). Gestione delle Sessioni: Salva automaticamente lo stato del grafo, la coda degli URL e l'elenco dei siti visitati, permettendo di interrompere e riprendere sessioni di crawling lunghe. Interfaccia di Monitoraggio: Una GUI basata su Tkinter fornisce controlli per avviare/fermare il processo e visualizza statistiche in tempo reale (numero di nodi/archi, URL in coda) e un log dettagliato delle operazioni. Tecnologie: Python, Tkinter, Selenium, spaCy, NetworkX, trafilatura. English 1. LUG-DB (Luigi Usai Graph Database) Title: LUG-DB: An Interactive Graph Database Viewer Description: LUG-DB (Luigi Usai Graph Database) is a lightweight desktop application, developed in Python with Tkinter, designed for the import, visualization, and interactive exploration of graph data. The tool is ideal for students, developers, and researchers who need a quick way to inspect graph-structured datasets without the overhead of installing and configuring a full-fledged database system. Key Features: In-Memory Graph Engine: Implements a simple in-memory property graph database, making the application self-contained and easy to run. Flexible Data Import: Supports data import from multiple formats: JSON: Includes an intelligent parsing logic capable of interpreting nested structures to automatically create nodes and hierarchical relationships. RDF/Turtle: Uses the rdflib library to parse standard Semantic Web formats like RDF/XML and Turtle (TTL), transforming triples into nodes, properties, and relationships. Canvas-Based Visualization: Intuitively represents graphs by drawing nodes (as circles colored according to their labels) and relationships (as labeled arrows). Direct Manipulation: Users can drag and reposition nodes directly on the canvas to improve the graph's readability. Simple GUI: Features an intuitive menu for importing files, viewing operation logs, and accessing software information. Technologies: Python, Tkinter, rdflib. 2. GraphRAG Silent Expander Title: GraphRAG Silent Expander: A Crawler for Automatically Building Knowledge Graphs from Web Sources Description: GraphRAG Silent Expander is a Python application that automates the process of crawling the web to extract named entities (people, organizations, locations) and their contextual relationships, dynamically building a knowledge graph. This tool is designed for researchers and developers in the fields of Information Extraction, Semantic Web, and data preparation for Retrieval-Augmented Generation (RAG) systems, providing a method to transform the unstructured web into a structured and interoperable knowledge base. Methodology and Features: Intelligent Web Crawling: Uses Selenium to navigate websites automatically. It politely manages a URL queue by cycling through domains and introducing delays to avoid overloading servers. Content Extraction: Relies on the trafilatura library to isolate and clean the main textual content of pages, discarding superfluous elements like menus, ads, and footers. Named Entity Recognition (NER): Analyzes the extracted text using spaCy's Italian language model to identify and classify entities. Graph Construction: The identified entities become nodes in a graph managed with NetworkX. Relationships (edges) are created between entities that co-occur within the same sentence, representing a strong contextual link. Semantic TTL Export: Its key feature is the ability to export the graph into Turtle (TTL) files. For each source URL, a separate TTL file is generated containing only the nodes and relationships extracted from that page, with entities mapped to standard RDF types (e.g., foaf:Person, geo:Place). Session Management: Automatically saves the state of the graph, the URL queue, and the list of visited sites, allowing long crawling sessions to be paused and resumed. Monitoring Interface: A Tkinter-based GUI provides controls to start/stop the process and displays real-time statistics (node/edge counts, URLs in queue) and a detailed operation log. Technologies: Python, Tkinter, Selenium, spaCy, NetworkX, trafilatura.
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Graph Database Management Systems
Kornelije Rabuzin, Martina Šestak · Advances in information quality and management · 2020
Nowadays, the increased amount and complexity of connected data stimulated by the appearance of social networks has shed a new light on the importance of managing such data, especially handling information about the connections. The most natural way of representing connected data is to represent them as nodes connected with relationships forming a graph. The idea of storing data as a set of nodes and edges comprising a graph was implemented in various forms in data models used in the past. The network data model, developed in late 1960s, can be considered as the first data model, which most accurately incorporated this idea. However, it was not long before the relational data model appeared, and took over the entire database market for years, which it dominates even nowadays. Therefore, the objective of this article is to give a timeline overview of developed graph data storage solutions in order to gain insight into past, present and future trends of GDBMSs. Additionally, the most influential factors and reasons for changes in trends in GDBMSs' usage will be analyzed.
Cited by 1
An Orthology Graph Database
Sarigol, Fatih · Zenodo (CERN European Organization for Nuclear Research) · 2024
More information about this dataset is available below: Topological mixing and irreversibility in animal chromosome evolution Darrin T. Schultz, Arno Blümel, Dalila Destanović, Fatih Sarigol, Oleg Simakov bioRxiv 2024.07.29.605683; doi: https://doi.org/10.1101/2024.07.29.605683 If you use this database in your study, please cite.
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Graph Database ResultSet Analysis
Michael Behrisch · OSF Preprints (OSF Preprints) · 2023
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Graph database and query.
Rosalba Giugno (46523), Vincenzo Bonnici (475167), Nicola Bombieri (475168) et al. · Figshare · 2015
<p>The database is composed by four graphs , , and . is the query. The occurrences of in the graphs are shown by colored vertices and bold edges.</p>
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An Orthology Graph Database
Sarigol, Fatih · Zenodo (CERN European Organization for Nuclear Research) · 2025
More information about this dataset is available below: Topological mixing and irreversibility in animal chromosome evolution Darrin T. Schultz, Arno Blümel, Dalila Destanović, Fatih Sarigol, Oleg Simakov bioRxiv 2024.07.29.605683; doi: https://doi.org/10.1101/2024.07.29.605683 If you use this database in your study, please cite.
Cited by 0Open access