Dynamic Metadata Configuration for RPD File Management
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Solution Overview
Problem
Existing business intelligence systems, such as Oracle Analytics Cloud and Oracle Business Intelligence Enterprise Edition, face challenges in efficiently updating and maintaining logical database schemas due to the complexity of compressed RPD files, especially in multi-tenant applications, leading to increased manual effort and difficulties in upgrading versions.
Innovation Solution
The solution involves generating and storing metadata configurations separately from the analytics instance, allowing for modular upgrades and enabling administrators to update configurations ahead of user instances, using a dynamic metadata configuration inclusion module to automatically integrate catalog files into RPD files, and generating compiled visualization images for business intelligence systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RPD files are stored within analytics instances, then data integrity is maintained, but system complexity increases and manual update effort increases
Solution Approach 1:
The patent separates RPD file storage from analytics instances by introducing a dedicated metadata repository. This segmentation allows RPD files to be stored independently in a specialized repository rather than being embedded within each analytics instance, reducing system complexity while maintaining data integrity through centralized management.
Solution Approach 2:
The patent introduces a metadata repository as an intermediary component between the analytics instances and the RPD files. This intermediary layer simplifies the system architecture by providing a dedicated storage mechanism for metadata, reducing the complexity of directly embedding RPD files within each analytics instance while ensuring data integrity through controlled access and management.
2Quantity of substance
If RPD files are compressed for storage efficiency, then storage space is optimized, but file updating and maintenance become difficult
Solution Approach 1:
The patent implements dynamic metadata configurations that allow the RPD file structure to be flexibly updated and modified. By using a metadata repository that supports dynamic configurations, the system enables easy updating and maintenance of RPD files without requiring full decompression and re-compression cycles, thus maintaining storage efficiency while improving updatability.
Solution Approach 2:
The patent utilizes parameter changes in the metadata repository to enable efficient updates of RPD files. By allowing metadata parameters to be dynamically modified and reconfigured, the system can update RPD files without compromising storage efficiency, as the metadata repository maintains optimized storage structures that accommodate changes without requiring full file decompression.
3Manufacturing precision
If manual updates are performed on RPD files, then configuration precision is maintained, but productivity decreases
Solution Approach 1:
The patent implements self-service capabilities through automated metadata extraction and configuration tools. The system automatically extracts metadata from source systems, validates configurations, and updates the RPD files in the metadata repository without requiring manual intervention for each update. This maintains configuration precision through automated validation while significantly improving update speed and productivity.
Solution Approach 2:
The patent replaces manual mechanical update processes with automated computational systems. By using automated tools for metadata extraction, validation, and RPD file generation, the system eliminates the need for manual configuration updates while maintaining precision through programmatic validation rules and automated error checking, thus大幅提升 productivity.
4Adaptability or versatility
If version upgrades are performed on analytics instances, then system functionality is improved, but maintenance complexity increases
Solution Approach 1:
The patent segments the analytics instance from the RPD file storage by introducing a separate metadata repository. This segmentation allows version upgrades of analytics instances to be performed independently without affecting the RPD files stored in the repository. The metadata repository serves as a stable foundation that can be updated separately, reducing maintenance complexity while enabling system functionality improvements.
Solution Approach 2:
The patent implements preliminary action by pre-configuring and validating metadata in the repository before deploying analytics instance upgrades. The metadata repository serves as a preparation layer where configurations are finalized and validated in advance, allowing smooth version upgrades of analytics instances without encountering configuration issues, thus reducing maintenance complexity while improving system functionality.
Data Source
AI summary
Embodiments generate changes to a logical model. Embodiments receive the changes in a configuration file, the changes comprising a declarative configuration, extract the changes and load the changes into a database and update a corresponding database model. Embodiments generate a first logical model that represents the database model and generate a second logical model that includes the changes. Embodiments generate automatically in a container using the declarative configuration a compiled visualization image from the second logical model, wherein the visualization image is adapted to be used by a business intelligence system to provide a visualization of data that incorporates the changes.