Graph Database and Relational Database Performance Comparison on a Transportation Network

2020 ◽  
pp. 407-418
Author(s):  
Jinhua Chen ◽  
Qingyu Song ◽  
Can Zhao ◽  
Zhiheng Li
Keyword(s):  
Relational Database ◽  
Transportation Network ◽  
Performance Comparison ◽  
Graph Database ◽  
Database Performance

scholarly journals Perbandingan Performa Relational, Document-Oriented dan Graph Database Pada Struktur Data Directed Acyclic Graph

2017 ◽  
Vol 8 (2) ◽  
Author(s):  
Pradana Setialana ◽  
Teguh Bharata Adji ◽  
Igi Ardiyanto
Keyword(s):  
Social Network ◽  
Directed Graph ◽  
Relational Database ◽  
Directed Acyclic Graph ◽  
Performance Test ◽  
Performance Comparison ◽  
Graph Database ◽  
Acyclic Graph ◽  
Database Performance ◽  
A Performance

Abstract. Directed Acyclic Graph (DAG) is a directed graph which is not cyclic and is usually employed in social network and data genealogy. Based on the characteristic of DAG data, a suitable database type should be evaluated and then chosen as a platform. A performance comparison among relational database (PostgreSQL), document-oriented database (MongoDB), and graph database (Neo4j) on a DAG dataset are then conducted to get the appropriate database type. The performance test is done on Node.js running on Windows 10 and uses the dataset that has 3910 nodes in single write synchronous (SWS) and single read (SR). The access performance of PostgreSQL is 0.64ms on SWS and 0.32ms on SR, MongoDB is 0.64ms on SWS and 4.59ms on SR, and Neo4j is 9.92ms on SWS and 8.92ms on SR. Hence, relational database (PostgreSQL) has better performance in the operation of SWS and SR than document-oriented database (MongoDB) and graph database (Neo4j).Keywords: database performance, directed acyclic graph, relational database, document-oriented database, graph database Abstrak. Directed Acyclic Graph (DAG) adalah graf berarah tanpa putaran yang dapat ditemui pada data jejaring sosial dan silsilah keluarga. Setiap jenis database memiliki performa yang berbeda sesuai dengan struktur data yang ditangani. Oleh karena itu perlu diketahui database yang tepat khususnya untuk data DAG. Tujuan penelitian ini adalah membandingkan performa dari relational database (PostgreSQL), document-oriented database (MongoDB) dan graph database (Neo4j) pada data DAG. Metode yang dilakukan adalah mengimplentasi dataset yang memiliki 3910 node dalam operasi single write synchronous (SWS) dan single read (SR) pada setiap database menggunakan Node.js dalam Windows 10. Hasil pengujian performa PostgreSQL dalam operasi SWS sebesar 0.64ms dan SR sebesar 0.32ms, performa MongoDB pada SWS sebesar 0.64ms dan SR sebesar 4.59ms sedangkan performa Neo4j pada operasi SWS sebesar 9.92ms dan SR sebesar 8.92ms. Hasil penelitian menunjukan bahwa relational database (PostgreSQL) memiliki performa terbaik dalam operasi SWS dan SR dibandingkan document-oriented database (MongoDB) dan graph database (Neo4j).Kata Kunci: performa database, directed acyclic graph, relational database, document-oriented database, graph database

scholarly journals The shortest path algorithm performance comparison in graph and relational database on a transportation network

2014 ◽  
Vol 26 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Mario Miler ◽  
Damir Medak ◽  
Dražen Odobašić
Keyword(s):  
Shortest Path ◽  
Road Network ◽  
Transportation Networks ◽  
Transportation Network ◽  
Database Systems ◽  
Performance Comparison ◽  
Graph Database ◽  
Shortest Path Algorithm ◽  
Algorithm Performance ◽  
Path Calculation

In the field of geoinformation and transportation science, the shortest path is calculated on graph data mostly found in road and transportation networks. This data is often stored in various database systems. Many applications dealing with transportation network require calculation of the shortest path. The objective of this research is to compare the performance of Dijkstra shortest path calculation in PostgreSQL (with pgRouting) and Neo4j graph database for the purpose of determining if there is any difference regarding the speed of the calculation. Benchmarking was done on commodity hardware using OpenStreetMap road network. The first assumption is that Neo4j graph database would be well suited for the shortest path calculation on transportation networks but this does not come without some cost. Memory proved to be an issue in Neo4j setup when dealing with larger transportation networks.

scholarly journals Map Reduce Based Optimized Frequent Subgraph Mining Algorithm for Large Graph Database

2020 ◽  
Vol 9 (3) ◽  
pp. 3131-3139
Keyword(s):  
Distributed System ◽  
Real World ◽  
Transportation Network ◽  
Vital Role ◽  
Graph Database ◽  
Frequent Subgraph Mining ◽  
Large Graph ◽  
Subgraph Mining ◽  
Frequent Subgraph ◽  
Centralized System

Distributed System, plays a vital role in Frequent Subgraph Mining (FSM) to extract frequent subgraph from Large Graph database. It help to reduce in memory requirements, computational costs as well as increase in data security by distributing resources across distributed sites, which may be homogeneous or heterogeneous. In this paper, we focus on the problem related complexity of data arises in centralized system by using MapReduce framework. We proposed a MapReduced based Optimized Frequent Subgrph Mining (MOFSM) algorithm in MapReduced framework for large graph database. We also compare our algorithm with existing methods using four real-world standard datasets to verify that better solution with respect to performance and scalability of algorithm. These algorithms are used to extract subgraphs in distributed system which is important in real-world applications, such as computer vision, social network analysis, bio-informatics, financial and transportation network.

Graph Database An Alternative for Relational Database

2019 ◽  
Vol 7 (2) ◽  
pp. 685-691
Author(s):  
H.R. Vyawahare1* ◽  
P.P. Karde2 ◽  
V.M. Thakare3
Keyword(s):  
Relational Database ◽  
Graph Database
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