Practical Solutions for Multimodal Data Retrieval
Challenges in Data Retrieval
Managing and retrieving data from multiple sources, such as text, audio, video, and images, becomes crucial as data volume and complexity increase, especially in sectors like artificial intelligence and big data analytics.
Existing Limitations
Current systems struggle to handle unstructured data effectively and execute complex queries across multiple formats.
Current Solutions
The Multimodal Data Retrieval Platform with Query-aware Feature Representation and Learned Index based on Data Lake (MQRLD) addresses these limitations by supporting flexible, transparent storage and introducing a multimodal data feature representation technique. The platform enables rich hybrid queries, optimizing the retrieval process across various data types while maintaining high performance in both accuracy and speed.
Performance Advantages
Performance tests showed that the platform outperformed traditional methods, demonstrating faster query times, higher accuracy rates, and a recall rate of 95% for complex multimodal queries.
API Support
The platform includes a multimodal open API (MOAPI) to perform hybrid queries across different data types, supporting several query types, including numeric equal, range, and vector-based nearest neighbor searches.
Value and Industry Applications
The MQRLD platform significantly advances multimodal data retrieval, providing significant benefits for industries like healthcare, law enforcement, and artificial intelligence applications.
AI Integration for Business Advancement
Discover how AI can redefine your way of work, stay competitive, and identify automation opportunities with MQRLD for efficient multimodal data retrieval.
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