MQRLD: A Multimodal Data Retrieval Platform With Query-aware Feature Representation And Learned Index Based On Data Lake

Abstract

Multimodal data has become a crucial element in the realm of big data analytics, driving advancements in data exploration, data mining, and empowering artificial intelligence applications. To support high-quality retrieval for these cutting-edge applications, a robust multimodal data retrieval platform should meet the challenges of transparent data storage, rich hybrid queries, effective feature representation, and high query efficiency. However, among the existing platforms, traditional schema-on-write systems, multi-model databases, vector databases, and data lakes, which are the primary options for multimodal data retrieval, make it difficult to fulfill these challenges simultaneously. Therefore, there is an urgent need to develop a more versatile multimodal data retrieval platform to address these issues. In this paper, we introduce a Multimodal Data Retrieval Platform with Query-aware Feature Representation and Learned Index based on Data Lake (MQRLD). It leverages the transparent

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