Cloud Extensibility Framework for Custom Data Model Segmentation
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Solution Overview
Problem
Software application providers on cloud platforms do not allow modification of base data models and processes, as these are publicly shared among all customers, making it impossible to extend them without direct modification, which limits customization and data storage options.
Innovation Solution
A cloud extensibility system that defines extension metadata for base data models, creating extended entities without modifying the underlying base models, allowing additional data models and processes to be stored at different service providers for enhanced customization and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If base data models and processes are publicly shared among all customers, then cloud platform reliability and stability are improved, but customization and extension capability deteriorate
Solution Approach 1:
The patent segments the data model into base data models (shared by all customers) and extended data models (customized per customer). This segmentation allows the base models to remain stable and shared while customers can create their own extensions without affecting the base models, thus resolving the contradiction between platform stability and customization capability.
Solution Approach 2:
The patent introduces an intermediary layer (extension metadata and extended entities) between the base data models and customer-specific requirements. This intermediary allows customers to extend base models without directly modifying them, maintaining platform stability while enabling customization through the extension mechanism.
2Adaptability or versatility
If base data models are modified to extend functionality, then customization capability is improved, but platform stability and data sharing reliability deteriorate
Solution Approach 1:
By segmenting data models into base and extended versions, the patent ensures that modifications to extended models do not affect the base models. This maintains data model consistency across the platform while allowing extensive customization capabilities for individual customers.
Solution Approach 2:
The patent creates copies of base data models as extended entities for each customer. These copies can be modified independently to meet customer-specific needs without affecting the original base models, thus maintaining platform reliability while enabling customization.
3Ease of operation
If data models are stored within the cloud platform, then accessibility is improved, but data privacy and storage control deteriorate
Solution Approach 1:
The patent introduces extension metadata as an intermediary that stores references to extended data models at different service providers. This allows the cloud platform to maintain accessibility while customers can store their extended data at their own chosen locations (on-premises or at different cloud providers), thus maintaining storage control flexibility and data privacy.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for extending data models and processes in a cloud platform. An embodiment operates by selecting an entity metadata in the cloud platform. The embodiment defines an extension metadata for the entity metadata. The embodiment defines an extended entity metadata based on the entity metadata and the extension metadata. The embodiment then creates an extended entity instance in the cloud platform based on the extended entity metadata.