Normalized Storage Model Construction and Query Optimization of Book Multi-source Heterogeneous Massive Data

According to the characteristics of massive, multi-source, heterogeneous, and rapid growth book literature data information from perspective metaverse, in order meet requirements efficient management retrieval such as standardized storage, effective extraction, scientific library construction for unstructured massive heterogeneous in-formation, this study focuses on normalization multi-source data, a warehouse model metaverse perspective, query optimization data. Systematic research implementation were conducted solve problem how process, manage, improving utilization value efficiency This utilized semi-structured features text construct an extraction rule effectively extracted information. Based HBase distributed storage structure parallel computing technology, scheme has been optimized improved ensure The experimental results show that compared with traditional methods, there are significant improvements multiple aspects accuracy recall rate methods