Semantic Model Extension via Data Introspection
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Solution Overview
Problem
Existing data analytics systems in enterprise environments, such as Oracle Fusion Applications and Oracle Analytics Cloud, require manual customization of semantic models for specific customer requirements, which is inefficient and labor-intensive, especially when customers need to classify, aggregate, or transform data for business intelligence purposes.
Innovation Solution
A system and method that introspects customer data in a data warehouse to determine custom facts, dimensions, and other model extensions, generating a payload to programmatically extend the semantic model, allowing for automated customization and surfacing of business intelligence data at the presentation layer without manual user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual customization of semantic models is performed to meet customer requirements, then the semantic model can be tailored to specific customer needs, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system performs self-service by automatically introspecting customer data in the data warehouse and generating semantic model extensions without requiring manual user input. The introspection process autonomously identifies custom facts, dimensions, and hierarchies by evaluating metadata and data patterns, then programmatically extends the semantic model to meet customer requirements.
Solution Approach 2:
The patent replaces the mechanical manual process of semantic model customization with an automated computational system. Instead of analysts manually examining data and creating model extensions, the system uses introspection algorithms to automatically analyze customer data, evaluate metadata, and generate the appropriate semantic model extensions programmatically.
2Productivity
If automated introspection is used to extend semantic models, then productivity is improved, but system complexity increases
Solution Approach 1:
The introspection process is segmented into distinct functional components: data evaluation, metadata analysis, custom fact identification, custom dimension identification, and hierarchy detection. Each component handles a specific aspect of the introspection task, making the overall complex process more manageable and maintainable while achieving high automation.
Solution Approach 2:
The system introduces an intermediary introspection layer between the customer data warehouse and the semantic model. This intermediary process evaluates metadata and data patterns to generate a representation of custom facts, dimensions, and hierarchies, which then serves as input for semantic model extension, simplifying the integration between data sources and analytical models.
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for extending a semantic model, for use with an analytic applications, analytics cloud, or other type of business intelligence or data analytics environment. A semantic model extension process introspects a customer's data, for example as stored in a data warehouse instance, and evaluates metadata associated therewith to determine custom facts, custom dimensions, and/or other types of data source model extensions. A payload or indication of such extensions is used to extend or customize a semantic model that enables surfacing of business intelligence or data analytics at a presentation layer. In accordance with an embodiment, the system can include an administrative console application and user interface that allows a user to view and validate a customer's data as loaded from their source environment into a data warehouse instance for use with other types of data analytics environments.