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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.19757 |
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| _version_ | 1866909803963482112 |
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| author | Yang, Jingyi Mo, Songsong Shi, Jiachen Yu, Zihao Shi, Kunhao Ding, Xuchen Cong, Gao |
| author_facet | Yang, Jingyi Mo, Songsong Shi, Jiachen Yu, Zihao Shi, Kunhao Ding, Xuchen Cong, Gao |
| contents | The explosive growth of multimodal data - spanning text, image, video, spatial, and relational modalities, coupled with the need for real-time semantic search and retrieval over these data - has outpaced the capabilities of existing multimodal and real-time database systems, which either lack efficient ingestion and continuous query capability, or fall short in supporting expressive hybrid analytics. We introduce ARCADE, a real-time data system that efficiently supports high-throughput ingestion and expressive hybrid and continuous query processing across diverse data types. ARCADE introduces unified disk-based secondary index on LSM-based storage for vector, spatial, and text data modalities, a comprehensive cost-based query optimizer for hybrid queries, and an incremental materialized view framework for efficient continuous queries. Built on open-source RocksDB storage and MySQL query engine, ARCADE outperforms leading multimodal data systems by up to 7.4x on read-heavy and 1.4x on write-heavy workloads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARCADE: A Real-Time Data System for Hybrid and Continuous Query Processing across Diverse Data Modalities Yang, Jingyi Mo, Songsong Shi, Jiachen Yu, Zihao Shi, Kunhao Ding, Xuchen Cong, Gao Databases Artificial Intelligence The explosive growth of multimodal data - spanning text, image, video, spatial, and relational modalities, coupled with the need for real-time semantic search and retrieval over these data - has outpaced the capabilities of existing multimodal and real-time database systems, which either lack efficient ingestion and continuous query capability, or fall short in supporting expressive hybrid analytics. We introduce ARCADE, a real-time data system that efficiently supports high-throughput ingestion and expressive hybrid and continuous query processing across diverse data types. ARCADE introduces unified disk-based secondary index on LSM-based storage for vector, spatial, and text data modalities, a comprehensive cost-based query optimizer for hybrid queries, and an incremental materialized view framework for efficient continuous queries. Built on open-source RocksDB storage and MySQL query engine, ARCADE outperforms leading multimodal data systems by up to 7.4x on read-heavy and 1.4x on write-heavy workloads. |
| title | ARCADE: A Real-Time Data System for Hybrid and Continuous Query Processing across Diverse Data Modalities |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2509.19757 |