| Autor: |
Heng Lin, Zhiyong Wang, Shipeng Qi, Xiaowei Zhu, Chuntao Hong, Wenguang Chen, Yingwei Luo |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Big Data Mining and Analytics, Vol 7, Iss 1, Pp 156-170 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2096-0654 |
| DOI: |
10.26599/BDMA.2023.9020015 |
| Popis: |
Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications, including financial risk assessment, commodity recommendation, and data lineage tracking. While the principles and design of these databases have been the subject of some investigation, there remains a lack of comprehensive examination of aspects such as storage layout, query language, and deployment. The present study focuses on the design and implementation of graph storage layout, with a particular emphasis on tree-structured key-value stores. We also examine different design choices in the graph storage layer and present our findings through the development of TuGraph, a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System (GDBMS). Additionally, TuGraph demonstrates superior performance in the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB) interactive benchmark. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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