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BigVectorBench: Heterogeneous Data Embedding and Compound Queries are Essential in Evaluating Vector Databases

Summary: BigVectorBench introduces an integrated benchmark for heterogeneous data embedding and compound (multimodal/constrained) queries—capabilities missing from existing vector DB evaluations. Experiments expose bottlenecks in mainstream systems and validate these workload choices. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14005
Venue
VLDB
Year
2025
Pagerank
5.5333312e-05
Overall Rank
7,843 | 46.20%
DOI
10.14778/3718057.3718078

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kang_vldb25,
        title = {{BigVectorBench: Heterogeneous Data Embedding and Compound Queries are Essential in Evaluating Vector Databases}},
        author = {Kang, Guoxin and Ge, Zhongxin and Hu, Jingpei and Zhang, Xueya and Wang, Lei and Zhan, Jianfeng},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {5},
        pages = {1536--1550},
        doi = {10.14778/3718057.3718078},
        url = {https://doi.org/10.14778/3718057.3718078},
        year = {2025}
}

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