Entity Group Analytics Processing via Pre-Annotation Segmentation
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Solution Overview
Problem
Data ingestion and annotation for entity groups in analytics processing is time-consuming and resource-intensive, requiring re-ingestion and re-annotation when entity groups are not initially annotated, which can be costly in terms of time and computing resources.
Innovation Solution
A method that intercepts entity groups in requests, expands them into individual entities that have been annotated, processes each entity individually, and merges the results to produce analytics for the entity group, thereby avoiding re-ingestion and reducing processing time and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data re-ingestion and re-annotation is performed for entity groups, then complete annotation coverage is achieved, but processing time and computing resources are significantly increased
Solution Approach 1:
The patent segments the entity group processing into two distinct phases: (1) individual entity annotation during initial data ingestion, and (2) group-level result aggregation after individual processing. This segmentation allows each entity to be annotated independently and stored, eliminating the need for time-consuming re-ingestion when entity groups are queried. The system divides the annotation task from the query task, resolving the contradiction between complete annotation coverage and processing time.
Solution Approach 2:
The patent performs preliminary annotation of individual entities during the initial data ingestion phase, before any entity group queries are executed. By pre-annotating all individual entities and storing their annotations, the system prepares the data in advance so that when entity groups are processed, the annotations are already available and can be simply aggregated. This preliminary action eliminates the need for re-ingestion and re-annotation, directly reducing processing time while maintaining annotation coverage.
2Manufacturing precision
If data re-ingestion and re-annotation is performed for entity groups, then complete annotation coverage is achieved, but computing resource consumption is significantly increased
Solution Approach 1:
The patent segments the annotation process into individual entity annotation (performed once during ingestion) and group result aggregation (performed during queries). This segmentation avoids the computationally expensive re-ingestion and re-annotation operations for entity groups, reducing computing resource consumption while maintaining complete annotation coverage through the stored individual entity annotations.
Solution Approach 2:
The patent creates and stores copies of individual entity annotations during the initial data ingestion phase. These annotation copies are preserved and can be efficiently retrieved and aggregated when entity groups are processed. By using these pre-created annotation copies instead of performing re-annotation, the system significantly reduces computing resource consumption while ensuring annotation coverage is maintained.
3Productivity
If individual entities are processed separately and results merged, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the processing workflow into distinct components: individual entity processing modules and a result aggregation module. Each entity can be processed independently through standardized interfaces, and the results are merged by a dedicated aggregation component. This segmentation improves processing efficiency through parallelization while managing system complexity by creating clear, modular boundaries between processing and aggregation functions.
Solution Approach 2:
The patent implements a universal result aggregation mechanism that can handle annotations from any entity type or group composition. The aggregation module serves multiple functions: combining individual entity results, handling various entity group configurations, and providing a unified interface for group-level queries. This universal approach improves processing efficiency across different scenarios while containing system complexity through a single, multi-functional aggregation component.
Data Source
AI summary
A computer system processes a group of inputs. A group of entities that is input for processing is intercepted. The intercepted group is expanded into individual entities. Each of the individual entities is processed to produce results for each individual entity. The results for each individual entity are intercepted and merged to produce results for the group of entities. Embodiments of the present invention further include a method and program product for processing a group of inputs in substantially the same manner described above.