ArcNeural: A Multi-Modal Database for the Gen-AI Era
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866912424710373376 |
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| author | Min, Wu Yuncong, Qiao Tan, Yu Yang, Chenghu |
| author_facet | Min, Wu Yuncong, Qiao Tan, Yu Yang, Chenghu |
| contents | ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integration of transaction and analytical processing. Experimental evaluations demonstrate ArcNeural's superior performance and scalability compared to state-of-the-art systems. This system bridges structured and unstructured data management, offering a versatile solution for enterprise-grade AI applications.
ArcNeural's design addresses the challenges of multimodal data processing, providing a robust framework for intelligent, data-driven solutions in the Gen AI era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ArcNeural: A Multi-Modal Database for the Gen-AI Era Min, Wu Yuncong, Qiao Tan, Yu Yang, Chenghu Databases ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integration of transaction and analytical processing. Experimental evaluations demonstrate ArcNeural's superior performance and scalability compared to state-of-the-art systems. This system bridges structured and unstructured data management, offering a versatile solution for enterprise-grade AI applications. ArcNeural's design addresses the challenges of multimodal data processing, providing a robust framework for intelligent, data-driven solutions in the Gen AI era. |
| title | ArcNeural: A Multi-Modal Database for the Gen-AI Era |
| topic | Databases |
| url | https://arxiv.org/abs/2506.09467 |