Administration Tool for BI Server Data Flexibility
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Solution Overview
Problem
Business intelligence (BI) servers face challenges in providing data flexibility and managing metadata extensions within data warehouses, as existing systems lack efficient tools for importing, mapping, and propagating changes across different data sources and layers.
Innovation Solution
An administration tool is introduced that imports data objects from an application framework into a physical model, maps them to a logical model, and publishes extension inputs to an extender, enabling metadata extensions and incremental updates in the data warehouse, utilizing a workflow that includes connection pools, drag-and-drop mapping, and interaction with a backend extender.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data objects are manually mapped and metadata extensions are manually managed in a BI server, then data flexibility and adaptability are limited, but system complexity and time consumption increase
Solution Approach 1:
The system performs preliminary actions by automatically importing data objects from the application framework into the physical model before manual intervention is needed. The administration tool pre-configures the import process, mapping data objects to the physical model structure in advance, so that when data flexibility is required, the foundation is already prepared and users only need to publish extension inputs rather than manually creating mappings from scratch
Solution Approach 2:
The administration tool serves as an intermediary between the application framework and the BI server's physical and logical models. It mediates the import process by automatically translating data objects from the application framework into the physical model format, and subsequently mapping them to the logical model. This intermediary automation reduces the time users would otherwise spend on manual mapping while maintaining data flexibility
2Adaptability or versatility
If comprehensive manual mapping and metadata management is performed across all data layers, then data flexibility improves, but device complexity increases
Solution Approach 1:
The system extracts the complex automation tasks of importing and mapping data objects from the manual process. The administration tool takes out the burdensome activities of manually configuring physical models, mapping to logical models, and managing metadata extensions, performing these tasks automatically. This extraction leaves users with a simplified interface where they only need to publish extension inputs, reducing perceived system complexity while maintaining data flexibility
Solution Approach 2:
The administration tool creates copies of data objects from the application framework and automatically instances them in the physical model. Rather than requiring users to manually recreate data object structures, the tool copies the essential structure and attributes from the source framework objects and instantiates them in the BI server's physical model, automatically handling the complexity of structural replication while preserving data flexibility
3Productivity
If automated import and mapping tools are implemented, then time consumption is reduced, but the ability to manage complex metadata extensions may be compromised
Solution Approach 1:
The system implements dynamic adaptability by allowing users to publish extension inputs that can modify the automatically generated physical and logical models. The automation provides a dynamic foundation that can be flexibly adjusted through the publish extension input process, enabling metadata extensions to adapt to complex requirements while maintaining high productivity. The system transitions from static automated generation to dynamic user-directed extension
Solution Approach 2:
The administration tool incorporates feedback mechanisms where users can review the automatically imported data objects and their mappings, then publish extension inputs to refine and extend the metadata. This feedback loop ensures that while automation handles the bulk of the work efficiently, users can provide corrections and extensions to meet complex metadata requirements, combining productivity with adaptability
Data Source
AI summary
An administration tool can be used to provide data flexibility in a business intelligence (BI) server that is associated with a data warehouse. The administration tool can display one or more data objects that are adapted to be imported from an application framework into a physical model maintained on the BI server. The administration tool can further map the one or more data objects in the physical model into a logical model, also maintained on the BI server, which corresponds to at least one target table in the data warehouse. Additionally, the administration tool can publish an extension input to an extender associated with the data warehouse based on the logical model, wherein the extender operates to generate one or more metadata extensions based on the extension input.