Fragmented Query Model for Dynamic Semantic Extensions
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Solution Overview
Problem
Existing data analytics environments lack the ability to dynamically accommodate custom data classification, aggregation, and transformation requirements of different customers, limiting their ability to provide tailored business intelligence and analytic applications.
Innovation Solution
A system and method that utilize a semantic layer with custom semantic extensions to extend a semantic data model, enabling a fragmented query model that dynamically merges changes at runtime to surface appropriate data based on the extended model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a semantic data model is extended with custom semantic extensions to meet different customer requirements, then adaptability is improved, but device complexity increases
Solution Approach 1:
The semantic data model is segmented into a base model and multiple custom semantic extensions. Each extension represents a specific customer requirement or customization, allowing the system to handle diverse data classification, aggregation, and transformation needs by combining the base model with relevant extensions rather than creating a completely custom model for each customer.
Solution Approach 2:
Custom semantic extensions are nested within the semantic data model structure. The extensions are organized in a hierarchical manner where base model concepts are extended by customer-specific definitions, creating a nested architecture that preserves the integrity of the base model while allowing customized behavior through nested extension layers.
2Adaptability or versatility
If custom semantic extensions are applied to extend the semantic data model, then adaptability is improved, but manufacturing precision deteriorates
Solution Approach 1:
Custom semantic extensions are defined and validated in advance before they are applied to the semantic data model. The system provides mechanisms for pre-defining extension schemas, data types, and transformation rules, ensuring that precision requirements are met before execution. This preliminary action allows for thorough testing and verification of extension correctness.
Solution Approach 2:
The system incorporates feedback mechanisms that validate custom semantic extensions against predefined constraints and schema definitions. When extensions are applied, the system checks for consistency, conflicts, and precision requirements, providing feedback that ensures only valid extensions are accepted into the semantic data model, thereby maintaining manufacturing precision.
3Adaptability or versatility
If a fragmented query model is used to dynamically merge changes at runtime, then adaptability is improved, but productivity deteriorates
Solution Approach 1:
The system pre-loads and caches the base semantic data model and pre-validates custom extensions during system initialization or during extension definition time. This preliminary preparation allows the runtime query processing to focus only on merging pre-validated extensions rather than validating everything from scratch, significantly improving productivity while maintaining adaptability.
Solution Approach 2:
The fragmented query model merges multiple custom semantic extensions with the base model in a unified runtime process. Instead of processing each extension separately, the system combines all active extensions and resolves their interactions in a single merge operation, reducing the overhead of multiple separate processing steps and improving overall productivity.
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for providing extensibility in an analytic applications environment, including a semantic layer that enables the use of custom semantic extensions to extend a semantic data model (semantic model). In accordance with an embodiment, the system enables use of a fragmented query model—when customizations are made to the semantic model, the system can dynamically merge the changes from the various deltas when queries are generated at runtime, to dynamically surface appropriate data based on the extended semantic model.