Elasticsearch for Complex Data Association Analysis: Modeling, Aggregation, and Optimization Techniques

Abstract

This study proposes a comprehensive methodology for complex data association analysis using Elasticsearch (ES), addressing the challenges of modeling, querying, and optimizing large-scale relational datasets. The proposed approach integrates multiple ES-specific techniques, including flexible data modeling with nested and join types, advanced aggregation frameworks for statistical analysis, and systematic query and index optimizations. Association strength between entities is quantified through co-occurrence metrics, implemented via ES aggregations such as terms and bucket_script, enabling efficient pattern discovery without sacrificing performance. Furthermore, the methodology emphasizes practical optimizations, such as sharding strategies, replica management, and careful aggregation design, to handle high-cardinality datasets and computationally intensive operations. The novelty lies in the holistic integration of these techniques, which collectively enhance ES’s capability for multi-dimensional association analysis while maintaining near real-time responsiveness. This work is particularly relevant for domains requiring hybrid full-text and structured data analysis, offering a scalable solution that balances analytical depth with system efficiency. Experimental validation demonstrates the method’s effectiveness in scenarios demanding both statistical rigor and operational agility, positioning ES as a viable alternative to traditional relational databases for complex association tasks. The findings provide actionable insights for practitioners seeking to leverage ES’s horizontal scalability and rich aggregation features in data-intensive applications.