BI Model Generation via Data Introspection and Curation
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Solution Overview
Problem
Traditional approaches to preparing business intelligence (BI) data models are less successful in addressing the complex schemas used in modern enterprise computing environments, leading to inefficiencies in building new subject areas or BI data models.
Innovation Solution
The system employs a combination of manually-curated artifacts and automatic generation of BI data models through data introspection, using a pipeline generator framework to evaluate dimensionality, degenerate attributes, and application measures, thereby creating an output target model and pipeline or load plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual approaches are used to prepare BI data models, then the models can be built with high precision and control, but the process requires excessive time and labor
Solution Approach 1:
The system enables self-service by allowing the BI model generation process to automatically introspect source data environments, evaluate dimensionality and attributes, and derive target models without extensive manual intervention. The pipeline generator framework autonomously performs data exploration and model construction, reducing dependency on manual expert analysis while maintaining model quality.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. The pipeline generator framework uses algorithmic approaches to introspect data schemas, evaluate dimensional properties, and generate BI models automatically, substituting the traditional manual mechanical process of model building with an automated intelligent system.
2Ease of manufacture
If traditional manual approaches are used to prepare BI data models, then the models can be customized precisely, but the complexity of the process increases significantly
Solution Approach 1:
The pipeline generator framework serves as an intermediary between the source data environment and the target BI model. It mediates the complex transformation process by automatically introspecting source schemas, evaluating dimensional attributes, and generating appropriate target models, thereby simplifying the overall process while maintaining precision.
Solution Approach 2:
The system automatically evaluates and adjusts key parameters such as dimensionality, attribute types, and model structure based on the introspected source data characteristics. By dynamically changing these parameters based on data analysis rather than manual specification, the system reduces process complexity while maintaining model precision.
Data Source
AI summary
In accordance with an embodiment, described herein are systems and methods for automatic generation of business intelligence (BI) data models using data introspection and curation, as may be used, for example, with enterprise resource planning (ERP) or other enterprise computing or data analytics environments. The described approach uses a combination of manually-curated artifacts, and automatic generation of a model through data introspection, of a source data environment, to derive a target BI data model. For example, a pipeline generator framework can evaluate the dimensionality of a transaction type, degenerate attributes, and application measures; and use the output of this process to create an output target model and pipeline or load plan. The systems and methods described herein provide a technical improvement in the building of new subject areas or a BI data model within much shorter periods of time.