Real-Time Entity Identifier Deduplication for High-Speed Data Streams

The patent segments the complex identifier linking problem into distinct processing stages: data ingestion from multiple sources, real-time clustering of identifiers, candidate pair generation, scoring and filtering, and feedback-based refinement. Each stage handles a specific aspect of the deduplication challenge, making the overall system manageable despite the complexity of handling multiple identifiers across diverse data sources.

The patent introduces an intermediary entity identifier that serves as a mediator between multiple source identifiers. This entity identifier acts as a unique representative that links various identifiers from different data sources, enabling holistic profile maintenance without directly managing the complexity of all possible identifier combinations across sources.

The patent performs preliminary actions by implementing real-time clustering and candidate pair identification as data streams arrive, rather than waiting to accumulate large datasets for batch processing. This preliminary filtering of obvious duplicates and groupings reduces the volume of data requiring intensive cleaning operations, thereby improving data quality while minimizing processing time delays.

The patent maintains continuous useful action through real-time processing pipelines that continuously ingest, cluster, and deduplicate data streams without interruption. This continuous processing approach ensures data quality improvement occurs progressively as data arrives, rather than requiring periodic batch cleaning operations that cause time delays and processing interruptions.

The patent applies partial action by implementing a multi-stage filtering approach where not all possible identifier pairs are processed with full computational intensity. Instead, the system performs initial clustering to identify candidate pairs, then applies scoring and filtering to focus computational power only on promising candidates, achieving high deduplication accuracy without exhaustively processing every possible combination at maximum computational power.

The patent applies local quality by adapting the level of processing intensity to the specific characteristics of different identifier pairs and data sources. The scoring and filtering mechanisms adjust computational effort based on local data properties, applying more rigorous analysis where needed and lighter processing where sufficient, thereby achieving accurate real-time deduplication with optimized computational power utilization.