Semantic Data Layer for Business Event Processing
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Solution Overview
Problem
Existing enterprise content management platforms are limited in their ability to utilize data beyond existing attributes of a current context data object for business event definitions and processing, restricting the scope of contextual input and report generation.
Innovation Solution
The integration of semantic data analysis to extend business dynamic data models, allowing for advanced business analytics results to be used in conditional expressions and as input for triggered business processes, enabling more comprehensive event generation and processing without requiring programming skills through tools like xCP Designer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing attributes of current context data objects are used for business event definitions, then the system maintains simplicity and ease of operation, but the scope and depth of business event processing is limited
Solution Approach 1:
The patent segments the data model into multiple layers: the existing business dynamic data model layer and a new semantic data layer. The semantic data layer contains extracted semantic information from documents and content, which can be independently developed and managed. This segmentation allows the system to expand processing scope using semantic data without complicating the original business data model structure.
Solution Approach 2:
The patent introduces a semantic data model as an intermediary layer between the existing business data and the business event processing logic. This semantic layer acts as a mediator that translates complex document content and metadata into structured semantic attributes that can be easily used in conditional expressions, thereby expanding processing capabilities without directly complicating the business logic layer.
2Adaptability or versatility
If semantic data analysis is integrated to extend business dynamic data models, then comprehensive event generation and processing is enabled, but programming complexity increases
Solution Approach 1:
The system implements self-service through automated semantic data extraction and integration. The semantic data model automatically extracts, processes, and integrates semantic information from documents and content sources without requiring manual programming for each extraction task. The framework provides built-in capabilities for semantic analysis, event generation, and processing, allowing the system to enhance its own functionality autonomously.
3Measurement precision
If advanced business analytics results are used in conditional expressions, then the precision and depth of business event detection is improved, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-extracting and pre-processing semantic data from documents and content sources before business events are triggered. The semantic data model performs advanced business analytics and prepares semantic attributes in advance, storing them in a structured format. When business events are evaluated, the system can directly use these pre-computed semantic attributes in conditional expressions without performing complex computations at event trigger time, thus maintaining high detection precision while managing computational complexity.
Data Source
AI summary
Semantic-based processing techniques are disclosed. Semantic processing of content data comprising a content item is performed. A semantic processing-triggered event is triggered based at least in part on the semantic processing. A responsive action is performed in response to the event. The responsive action may include launching a business process, providing a result of semantic processing as an input, or augmenting a content item metadata set with a result of semantic processing.