Baseline Model Configurator for Rapid Data Hub Customization
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Solution Overview
Problem
The manual re-configuration of large and complex data models requires extensive time, resources, and specialized knowledge, making it difficult to efficiently modify applications to meet specific user needs.
Innovation Solution
An application configurator provides a guided setup interface and system compiler to automatically reconfigure baseline models based on user inputs, reducing the time and knowledge required for modifications by using pre-programmed configurations and reference objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual re-configuration of data models is performed, then customization to user needs is achieved, but extensive time and specialized knowledge are required
Solution Approach 1:
The system performs preliminary actions by pre-configuring baseline models with standard data models, dimensions, modules, and relationships before user deployment. This allows users to start with a pre-prepared foundation that can be quickly customized rather than building from scratch, significantly reducing the time and expertise needed for configuration.
Solution Approach 2:
The system creates copies of proven baseline models that can be replicated and customized for different users. Instead of manually re-configuring data models each time, the system copies existing validated models and allows users to modify them, reducing both time and the need for specialized knowledge while maintaining adaptability.
2Adaptability or versatility
If manual re-configuration of data models is performed, then customization to user needs is achieved, but extensive specialized knowledge is required
Solution Approach 1:
The system implements universal baseline models that serve multiple functions and user needs. These baseline models contain standard data models, dimensions, and modules that can be applied across different scenarios with minimal customization. Users can achieve adaptability through simple selection and configuration rather than deep technical knowledge.
Solution Approach 2:
The system enables self-service configuration where users can customize their own data models using guided interfaces and templates. The baseline models are designed to be self-explanatory with automatic relationship establishment, allowing users to perform customization without requiring specialized knowledge of data modeling concepts.
3Productivity
If baseline models are used with application configurator, then re-implementation time is reduced to weeks, but automation of configuration process is required
Solution Approach 1:
The system replaces manual mechanical configuration processes with automated computational processes. The application configurator uses algorithms to automatically generate data models, establish relationships between dimensions and modules, and configure baseline models based on user inputs. This substitution of manual operations with automated systems enables rapid re-implementation while maintaining high customization capability.
Data Source
AI summary
A system for managing data includes an application configurator that is configured to obtain, from the client device, a request for a new generated application, wherein the set of configured models is based on a baseline model. In response to the request, the application configurator initiates a guided setup using an user interface to obtain a set of configuration selections for generating the set of configured models, generates the set of configured models based on the set of configuration selections, and apply a set of application programming interface (API) calls to the baseline model to generate a data hub and a spoke model. The data hub and spoke model is provided to a client device requesting the set of configured models.