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Accurate SPARQL generation via in-context learning and schema-based query construction. [PDF]

Bioinformatics
Nagazumi H, Moriya Y, Kawashima S, Katayama T, Shimizu K.   +4 more
europepmc   +1 more source

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A graph database for life cycle inventory using Neo4j

Journal of Cleaner Production, 2023
Mohamed Saad, Yingzhong Zhang
exaly   +2 more sources

IFC-Based 4D Construction Management Information Model of Prefabricated Buildings and Its Application in Graph Database

Applied Sciences (Switzerland), 2021
Effective data interoperability and schedule analysis play a significant role in improving the management of prefabricated buildings. However, there is a lack of efficient strategies and comprehensive approaches for data interoperability and data-based ...
Bin Yang, Miaosi Dong, Boda Liu
exaly   +2 more sources

GDsmith: Detecting Bugs in Cypher Graph Database Engines

International Symposium on Software Testing and Analysis, 2023
Graph database engines stand out in the era of big data for their efficiency of modeling and processing linked data. To assure high quality of graph database engines, it is highly critical to conduct automatic test generation for graph database engines ...
Ziyue Hua, Weisheng Lin, Luyao Ren, Zongyang Li, Lu Zhang, Wenpin Jiao, Tao Xie   +6 more
semanticscholar   +1 more source

Testing Graph Database Systems via Graph-Aware Metamorphic Relations

Proceedings of the VLDB Endowment, 2023
Graph database systems (GDBs) have supported many important real-world applications such as social networks, logistics, and path planning. Meanwhile, logic bugs are also prevalent in GDBs, leading to incorrect results and severe consequences.
Zeyang Zhuang, Penghui Li, Pingchuan Ma, Wei Meng, Shuai Wang   +4 more
semanticscholar   +1 more source

Finding bugs in Gremlin-based graph database systems via Randomized differential testing

International Symposium on Software Testing and Analysis, 2022
Graph database systems (GDBs) allow efficiently storing and retrieving graph data, and have become the critical component in many applications, e.g., knowledge graphs, social networks, and fraud detection.
Yingying Zheng, Wensheng Dou, Yicheng Wang, Zheng Qin, Leile Tang, Yu Gao, Dong Wang, Wei Wang, Jun Wei   +8 more
semanticscholar   +1 more source

ByteGraph: A High-Performance Distributed Graph Database in ByteDance

Proceedings of the VLDB Endowment, 2022
Most products at ByteDance, e.g., TikTok, Douyin, and Toutiao, naturally generate massive amounts of graph data. To efficiently store, query and update massive graph data is challenging for the broad range of products at ByteDance with various ...
Changji Li, Hongzhi Chen, Shuai Zhang, Ying-Xin Hu, Chao Chen, Zhenjie Zhang, Meng, Li, Xiangcheng Li, Dongqing Han, Xiaohui Chen, Xudong Wang, Hui-dong Zhu, Xu-Dong Fu, Ting Wu, Hongfei Tan, Hengtian Ding, Meng-Ti Liu, Kangsheng Wang, Ting Ye, Lei Li, Xin Li, Yu Wang, Chen Zheng, Han Yang, James Cheng   +25 more
semanticscholar   +1 more source

Query-based Performance Comparison of Graph Database and Relational Database

Symposium on Information and Communication Technology, 2022
A graph database is a type of NoSQL database that uses graph structure for semantic queries with nodes, edges, and properties to represent and store data.
T. Do, Thai-Bao Mai-Hoang, Van-Quyet Nguyen, Quyet-Thang Huynh   +3 more
semanticscholar   +1 more source