MQRLD: A Multimodal Data Retrieval Platform with Query-aware Feature Representation and Learned Index Based on Data Lake
Ming Sheng, Shuliang Wang, Yong Zhang, Kaige Wang, Jingyi Wang, Yi Luo, and Rui Hao

TL;DR
This paper presents MQRLD, a versatile multimodal data retrieval platform that combines data lake storage, query-aware feature representation, and learned indexes to enhance retrieval efficiency and support complex hybrid queries.
Contribution
The paper introduces MQRLD, a novel multimodal data retrieval platform integrating data lake storage, a query-aware feature strategy, and learned indexes for improved performance.
Findings
MQRLD outperforms existing methods in query efficiency.
It effectively supports rich hybrid queries.
The platform demonstrates significant retrieval performance improvements.
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…
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Taxonomy
TopicsData Quality and Management · Semantic Web and Ontologies · Data Mining Algorithms and Applications
