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BigVectorBench: Heterogeneous Data Embedding and Compound Queries are Essential in Evaluating Vector Databases
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DOI：10.14778/3718057.3718078.png)
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Abstract
En 中文
Vector databases are designed to effectively store, organize, and retrieve high-dimensional vectors, enabling faster and more accurate querying and analysis. This study highlights that the performance of cutting-edge vector databases hinges on their proficiency in managing heterogeneous data embedding and handling compound queries. The former task revolves around converting varied data types into a cohesive vector format, while the latter involves processing multimodal or single-modal queries with precise constraints. The paper advocates for evaluating these dual tasks within an integrated benchmark framework. However, state-of-the-art vector database benchmarks overlook heterogeneous data embedding and compound queries, creating a gap in evaluating vector database performance.
Keywords:
vector databases
heterogeneous data embedding
compound queries
benchmark framework
high-dimensional vectors
Journal
P
IF:
3.3
Papers:
556
Citations:
1.2W
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