Caching Partition Identifiers for Fast Geometric Pattern Matching

Pattern searching in large datasets is fundamental across scientific domains, as in bioinformatics or geographic information systems. As datasets grow and search complexity increases, these operations often result in long-running queries. While various approaches exist to improve query execution times, they face limitations when dealing with complex geometric patterns in comprehensive datasets. This paper presents a novel caching-based architecture combined with query decomposition to significantly reduce computation time for geometric pattern searches in large partitioned datasets. We focus on GeoMine, a software tool for searching protein-ligand complexes in life sciences, but demonstrate the approach's broader applicability. Our key innovation lies in recomposing queries into smaller subvariants, which are separately executed against the database. The results are stored as an index-like structure, allowing subsequent new queries to utilize this cache to reduce the search space to relevant partitions. We evaluated our approach on a lightweight variant of GeoMine and tested various storage backends, including RocksDB, Redis, and PostgreSQL. With this approach, we could substantially reduce the computation time for our use case. We further applied our approach to geospatial data using an OpenStreetMap dataset to demonstrate transferability and show its potential beyond its original bioinformatics use case. Our work contributes to more efficient data exploration in scientific research, accelerating queries on datasets often occurring in the scientific domain.