Target Tree Generation for CMDB Data Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Configuration management databases (CMDBs) face challenges in efficiently converting and integrating data from diverse data structures into a format usable by target systems, limiting effective data utilization and integration across different environments.
Innovation Solution
A method involving the construction of a mapping file with definitions of a target tree structure, attributes, and variables, which enables the generation of a target data tree from source data in a CMDB, allowing for data extraction and organization based on specific attributes and variables tailored to the target system's requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data conversion and integration methods are used to transform source data structures into target formats, then data utilization and integration capabilities are enhanced, but the complexity of handling diverse data structures increases
Solution Approach 1:
The patent introduces an intermediary data conversion layer that mediates between diverse source data structures and the target system. This intermediary layer provides standardized conversion routines and mapping mechanisms that transform various source formats into a unified target format, enabling data integration without directly coupling source and target systems. The intermediary approach resolves the contradiction by abstracting the complexity of handling diverse data structures away from the core integration logic.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting conversion parameters and transformation rules based on the specific source data structure being processed. The system modifies conversion parameters such as data mapping relationships, transformation algorithms, and validation rules to accommodate different source formats. This allows the same integration framework to handle diverse data structures by changing its operational parameters rather than its fundamental structure, thereby maintaining adaptability while controlling complexity.
2Loss of information
If comprehensive data extraction and organization is performed to meet target system requirements, then data utilization is enhanced, but the time and resources required for data processing increase
Solution Approach 1:
The patent applies preliminary action by pre-defining data extraction rules, transformation templates, and organization schemas before actual data processing occurs. The system establishes conversion configurations, mapping relationships, and validation criteria in advance, so that when source data needs to be transformed, the processing can proceed efficiently using pre-prepared instructions. This reduces processing time while ensuring comprehensive data extraction and organization meets target system requirements.
Solution Approach 2:
The patent segments the data processing workflow into distinct modular stages: data extraction, data transformation, data validation, and data loading. Each stage handles specific aspects of data conversion independently with dedicated processing logic. This segmentation allows parallel processing of different data streams, enables selective application of processing steps based on data type, and facilitates optimization of individual stages without affecting the entire processing pipeline, thereby reducing overall processing time while maintaining data usability.
Data Source
AI summary
Target tree generation can include constructing a mapping file comprising a definition of a target tree and generating the target tree using source data and the mapping file.