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http://dx.doi.org/10.5392/JKCA.2021.21.12.084

Graph Database Benchmarking Systems Supporting Diversity  

Choi, Do-Jin (충북대학교 정보통신공학과)
Baek, Yeon-Hee (충북대학교 빅데이터학과)
Lee, So-Min (충북대학교 정보통신공학과)
Kim, Yun-A (충북대학교 빅데이터학과)
Kim, Nam-Young (충북대학교 빅데이터학과)
Choi, Jae-Young (충북대학교 정보통신공학과)
Lee, Hyeon-Byeong (충북대학교 정보통신공학과)
Lim, Jong-Tae (충북대학교 정보통신공학과)
Bok, Kyoung-Soo (원광대학교 SW융합학과)
Song, Seok-Il (한국교통대학교 컴퓨터공학과)
Yoo, Jae-Soo (충북대학교 정보통신공학과)
Publication Information
The Journal of the Korea Contents Association / v.21, no.12, 2021 , pp. 84-94 More about this Journal
Abstract
Graph databases have been developed to efficiently store and query graph data composed of vertices and edges to express relationships between objects. Since the query types of graph database show very different characteristics from traditional NoSQL databases, benchmarking tools suitable for graph databases to verify the performance of the graph database are needed. In this paper, we propose an efficient graph database benchmarking system that supports diversity in graph inputs and queries. The proposed system utilizes OrientDB to conduct benchmarking for graph databases. In order to support the diversity of input graphs and query graphs, we use LDBC that is an existing graph data generation tool. We demonstrate the feasibility and effectiveness of the proposed scheme through analysis of benchmarking results. As a result of performance evaluation, it has been shown that the proposed system can generate customizable synthetic graph data, and benchmarking can be performed based on the generated graph data.
Keywords
Bigdata; Database; Benchmarking; Workload; NoSQL;
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