Asset Attribute Data Mapping Between BDNA and IBM CMDB
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Solution Overview
Problem
Large companies and governmental entities face challenges in accurately and efficiently managing their assets due to the difficulty in manually collecting and consolidating data, particularly when using different automated inventory systems like IBM Tivoli CMDB and BDNA software, which have incompatible data models and semantics.
Innovation Solution
A system is developed to map and transform asset attribute data from one data model to another, using BQL reports and transformation rules to extract, transform, and export data from BDNA systems into IBM CMDB formats, ensuring data consistency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated inventory collection is performed using systems like BDNA software, then data collection efficiency is improved, but data model compatibility with external systems like IBM CMDB deteriorates
Solution Approach 1:
The patent implements a mapping system that acts as an intermediary layer between the BDNA automated inventory system and external systems like IBM CMDB. This mapping system translates data models, attributes, and relationships from one format to another, enabling compatibility without requiring changes to the core BDNA system. The mapping approach allows automated data collection to continue while adapting the output to various external system requirements.
2Adaptability or versatility
If manual data collection is performed, then data model compatibility is maintained, but time consumption and cost increase
Solution Approach 1:
The patent establishes mapping configurations and transformation rules in advance before actual data collection occurs. By pre-defining how data from BDNA should be transformed to match various external system formats, the system eliminates the need for manual data model adaptation during or after collection. This preliminary setup enables automated, efficient data extraction while maintaining compatibility with multiple external systems.
3Extent of automation
If data is extracted from BDNA repositories, then automated inventory capability is maintained, but data transformation complexity increases
Solution Approach 1:
The patent breaks down the data transformation process into distinct, manageable components: extraction of raw data from BDNA repositories, transformation through mapping rules, and export to external systems. Each component handles a specific aspect of the data flow, making the overall complex process more manageable and maintainable. The segmentation allows independent development and testing of mapping rules for different data types and target systems.
Data Source
AI summary
Methods and apparatus to transform attribute data about assets in a source system data model into attribute data about the same assets in a target system data model. The first step is to extract the necessary attribute data from attribute data collected about inventory assets of a business entity needed to populate the attributes in objects representing those inventory assets in a target system data model. Transformation rules are written which are designed to make all conversions necessary in semantics, units of measure, etc. to transform the source system attribute data into attribute data for the target system which has the proper data format. These transformation rules are executed on a computer on the extracted attribute data and the transformed attribute data is stored in an ER model. In the preferred embodiment, the transformation rules are object-oriented in that transformation rules for subtypes can be inherited from their parent types or classes. An export adapter which is capable of invoking the application programmatic interface of the target system CMDB is then used to export the transformed attribute data stored in the ER model to the target system CMDB.