Context-Sensitive Data Reconciliation in CMDB Systems
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Solution Overview
Problem
Conventional configuration management database (CMDB) systems face difficulties in storing and retrieving context-sensitive data from multiple data sources, leading to incomplete provider-specific data in target datasets due to the need for separate queries and transformation/merge operations, which are not scalable and may lose provider-specific data.
Innovation Solution
A management system incorporating a reconciliation engine and a context-sensitive query engine that reconciles resource objects from different data providers, storing them in separate partitions and allowing queries to retrieve provider-specific data based on context information, using a common key and reconciliation identifiers to merge and retrieve data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate queries and transformation/merge operations are used to combine data from multiple partitions, then data from different data sources can be retrieved, but the system becomes more difficult to use and provider-specific data may be lost
Solution Approach 1:
The system segments data storage by creating separate provider-specific data partitions while maintaining a unified query interface. Each partition stores data with provider-specific context attributes intact, allowing the system to retrieve complete provider-specific data without requiring complex manual merge operations.
Solution Approach 2:
The patent introduces context attributes as intermediaries that carry provider-specific information through the data reconciliation and storage process. These context attributes act as mediators that preserve provider identity and specific data characteristics, enabling the query engine to retrieve provider-specific data directly without complex transformation operations.
2Productivity
If merge precedence rules are applied during reconciliation, then resource objects can be merged from multiple sources, but provider-specific attribute content may not be presented in the target dataset
Solution Approach 1:
The system applies local quality by preserving provider-specific data characteristics within context attributes during reconciliation. Instead of applying uniform merge rules that lose provider-specific information, the system maintains the unique qualities of each provider's data by storing them with their associated context attributes in the target partition.
Solution Approach 2:
The patent changes the parameter representation by introducing context attributes that encode provider-specific information. This parameter transformation allows the system to maintain multiple provider-specific versions of attributes rather than losing them during reconciliation, enabling later retrieval of provider-specific data based on context information.
3Reliability
If workarounds are added by creating new attributes to ensure provider-specific data inclusion, then data completeness may be improved, but the approach is not entirely scalable
Solution Approach 1:
The system implements universality by creating a standardized context attribute mechanism that works across all provider-specific data scenarios. Rather than creating ad-hoc new attributes for each provider-specific data requirement, the context attribute framework provides a universal solution that maintains data completeness while remaining scalable to any number of data providers.
Data Source
AI summary
A management system may include a reconciliation engine configured to reconcile a first instance of a resource object from a first data provider and a second instance of the resource object from a second data provider to obtain a reconciled resource object, and store the first instance, and second instance, and the reconciled resource object in datasets. The management system may include a context sensitive query engine configured to receive a context-sensitive query including context information identifying a source originally providing context sensitive data associated with a context-sensitive attribute, and retrieve the context sensitive data from one or more of the datasets based on the context information.