Research on Intelligent Analysis and Visualization Technology of Big Data for Massive Heterogeneous Data Processing

Abstract

The research puts forward solutions from three levels: in the aspect of heterogeneous data fusion, a semantic alignment model based on dynamic Knowledge Graph (KG) is constructed, and semantic conflicts are solved through multimodal ontology modeling and dynamic entity alignment mechanism (fusion of structure, semantics and temporal similarity); On the level of intelligent analysis, a collaborative hybrid computing framework based on improved Lambda architecture is designed, which combines real-time processing of sliding window and deep mining of batch data lakes, and realizes the optimization of results through dynamic weight fusion. In the visual interaction, a three-tier engine integrating progressive rendering and predictive loading is developed. By using quadtree index, dynamic level of detail (LOD) and LSTM behavior prediction, the interaction delay at 1080P resolution is controlled within 100ms. The experimental results show that the cross-modal link accuracy rate of this technical scheme is 92.7%, the semantic conflict resolution rate is 91.2%, the batch processing delay is stable at 210±15ms, the throughput is increased by 18.75%, and the visual interaction delay is significantly reduced, which achieves a performance breakthrough in theory and application.