The LDBC Financial Benchmark: Transaction Workload

Graph databases play a pivotal role in the FinTech industry. However, existing graph benchmarks fail to capture the unique characteristics of financial datasets and workloads, rendering them inadequate for evaluating graph databases in financial scenarios. This paper presents the LDBC Financial Benchmark (FinBench) Transaction Workload, a novel benchmark that adopts a choke point-driven design methodology, emphasizing performance bottlenecks, and incorporates distinct features such as dataset skewness, edge multiplicity, temporal window filtering, recursive path filtering, read-write query patterns, and truncation on hub vertices. Key contributions include a scalable data generator that synthesizes datasets with financial-specific features, a parameter generator that leverages bucketed data statistics for runtime consistency across queries, and a scalable benchmark driver that biases query execution by time windows. Experimental evaluations on graph databases demonstrate the benchmark's capability to reveal novel choke points and provide insights into system performance in financial scenarios.

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Publication Details

Journal
Proceedings of the VLDB Endowment
Published
2025-05-01
DOI
https://doi.org/10.14778/3746405.3746424
Citations
1
Primary Topic
Graph Theory and Algorithms
Type
article
Field-Weighted Citation Impact
1.28
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article

The LDBC Financial Benchmark: Transaction Workload

Wenguang Chen, Jia‐Mian Hu, Gábor Szárnyas, Lei Zou et al.
1 citations
Proceedings of the VLDB Endowment
Graph Theory and Algorithms
1.28
article

The LDBC Financial Benchmark: Transaction Workload

Wenguang Chen, Jia‐Mian Hu, Gábor Szárnyas, Lei Zou, Chuntao Hong, Tao Lv, Zhihui Guo, Jia Li, Ke Huang, Yan Zhou, Wei Yuan, Qiang Yin, Xiaowei Zhu, Yue Pang, Youren Shen, Heng Lin, Shipeng Qi, Bing Tong, Yongwei Wu, Xujin Ba, Songlin Lyu
article en
1 citations

Abstract

Graph databases play a pivotal role in the FinTech industry. However, existing graph benchmarks fail to capture the unique characteristics of financial datasets and workloads, rendering them inadequate for evaluating graph databases in financial scenarios. This paper presents the LDBC Financial Benchmark (FinBench) Transaction Workload, a novel benchmark that adopts a choke point-driven design methodology, emphasizing performance bottlenecks, and incorporates distinct features such as dataset skewness, edge multiplicity, temporal window filtering, recursive path filtering, read-write query patterns, and truncation on hub vertices. Key contributions include a scalable data generator that synthesizes datasets with financial-specific features, a parameter generator that leverages bucketed data statistics for runtime consistency across queries, and a scalable benchmark driver that biases query execution by time windows. Experimental evaluations on graph databases demonstrate the benchmark's capability to reveal novel choke points and provide insights into system performance in financial scenarios.

Proceedings of the VLDB EndowmentVol. 18(9)
Jiangsu University (CN), Shanghai Jiao Tong University (CN), Peking University (CN), GfK (United States) (US), Leo Baeck College (GB), China Guangzhou Analysis and Testing Center (CN), Antea Group (France) (FR), Yixing People's Hospital (CN), CREATe Centre (GB), University of Hong Kong (HK), Tsinghua University (CN)
Openalex Percentile: Top 16%
Graph Theory and Algorithms
1.28
DOISourceOpenAlex
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