Single-Pass Entity Expansion and Grouping for Data Analysis
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Solution Overview
Problem
Existing entity analysis techniques are computationally expensive due to the need for multiple processing passes to identify and group entities, resulting in high computational load.
Innovation Solution
A computer-readable storage medium with executable instructions that converts entities into a standard form with normalized attributes and features, expands them into variants, and assigns them to groups in a single pass using an entity expander and grouper, respectively, with state machines and finite state networks for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule based processing is used to identify entity relationships, then entity analysis accuracy is improved, but computational load increases due to multiple processing passes
Solution Approach 1:
The patent applies preliminary action by performing entity expansion and variant generation before the main grouping process. By pre-processing entities to create comprehensive variant forms and relationships in advance, the system eliminates the need for multiple re-processing passes, thereby maintaining high accuracy while reducing computational load.
Solution Approach 2:
The patent segments the entity analysis process into distinct modular stages: entity identification, entity expansion, variant generation, and grouping. This segmentation allows each stage to be optimized independently and processed efficiently in a single pass, reducing the overall computational burden while maintaining accuracy.
2Measurement precision
If multiple processing passes are performed over entities, then entity relationship identification is improved, but processing time increases
Solution Approach 1:
The system performs preliminary entity expansion and relationship mapping before the main processing pass. By preparing expanded entity forms and potential relationships in advance, the system can identify all entity relationships in a single pass through the data, eliminating the need for multiple processing passes and reducing processing time.
Solution Approach 2:
The patent implements continuous useful action by processing entities in a single uninterrupted pass through the data stream. The system maintains running data structures that allow relationship identification to occur continuously as entities are encountered, rather than requiring repeated passes over the same data.
3Loss of information
If comprehensive entity analysis is performed, then Business Intelligence quality is improved, but computational expense increases
Solution Approach 1:
The patent segments the comprehensive entity analysis into modular components: entity extraction, expansion, annotation, and grouping. Each module performs a specific function with optimized computational requirements, allowing the system to maintain high Business Intelligence quality while reducing overall computational expense through efficient resource utilization.
Solution Approach 2:
The system performs preliminary entity expansion and variant generation to create a comprehensive view of all possible entity forms and relationships before the main analysis. This pre-processing ensures that no potential Business Intelligence is missed during the single-pass processing, while the computational expense is distributed more efficiently across preprocessing and main processing stages.
Data Source
AI summary
A computer readable storage medium includes executable instructions to convert an entity to a standard form including normalized attributes, a tag reference and a feature. The entity is expanded with corresponding variants. The standard form and corresponding variants are combined to form an annotated entity in a first processing step. The entity is assigned to a group in a second processing step that accesses the annotated entity. The entity is processed in a single pass comprising the first processing step and the second processing step.