Hybrid Query

Hybrid querying integrates structured data from relational databases with unstructured data from sources like large language models (LLMs) or vector embeddings to answer complex questions exceeding the capabilities of either system alone. Current research focuses on efficient algorithms and data structures, such as novel proximity graphs and workload-aware partitioning schemes, to handle the combined search across different data types and improve query throughput. This approach promises to significantly enhance the capabilities of data management systems, enabling more comprehensive and insightful analyses across diverse data sources for applications ranging from knowledge graph exploration to design optimization.