Multi-Layer Symbol-Space Compression for Balanced Pattern Handling

The patent segments the pattern space into multiple data layers, where each layer captures patterns at different levels of abstraction. Common patterns are extracted and represented as compressed symbols in higher layers, while rare patterns remain in lower layers. This segmentation allows the system to distribute different data layers across multiple machines based on their access patterns, improving scalability while maintaining accuracy through hierarchical pattern representation.

The patent introduces a hierarchical dimension to pattern representation by creating multiple data layers. Instead of treating all patterns at the same level, the system organizes them in layers where L0 contains original patterns and L1-Ln contain progressively more compressed and abstracted patterns. This dimensional transformation allows the system to handle pattern correlations more effectively and distribute workload across machines based on layer-specific access patterns.

The patent implements self-service by automatically generating the mapping between patterns and compressed symbols through the data layer construction process. The system autonomously identifies common patterns, creates compressed representations, and builds the mapping tables without manual intervention. This automated approach maintains storage efficiency while eliminating the complexity associated with manual symbol compression.

The patent performs preliminary action by pre-processing the pattern data to extract common patterns and create compressed symbol representations before the actual pattern recognition tasks. The data layer construction process pre-computes the mapping between original patterns and compressed symbols, which is then reused during pattern matching operations. This preliminary compression improves storage efficiency while the automated nature of the process avoids manual intervention complexity.