Statistical Rules Engine for CMDB Reconciliation
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Solution Overview
Problem
Manual identification of configuration items (CIs) in ITIL-based Configuration Management Databases (CMDBs) is time-consuming and inefficient, as it requires significant human intervention to reconcile duplicate objects across multiple datasets, leading to lengthy reconciliation processes.
Innovation Solution
A method utilizing statistical rules to identify and merge CIs across datasets, where a statistical rules engine applies rules to mark objects as identified based on threshold values, reducing the need for manual identification by generating hints and adjusting rule probabilities based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification of CIs is used to ensure accurate matching and data integrity, then reliability is improved, but productivity deteriorates due to time-consuming human intervention
Solution Approach 1:
The system performs self-identification of CIs by automatically evaluating statistical rules and threshold values without requiring manual human intervention. The reconciliation engine autonomously compares configuration items across datasets, evaluates matching rules, and identifies duplicates, enabling the system to serve itself rather than relying on external human operators.
Solution Approach 2:
The patent replaces the mechanical manual process of human operators reviewing and identifying CIs with an automated computational system. The reconciliation engine uses statistical rules, threshold evaluations, and algorithmic comparisons to substitute human cognitive and manual labor with automated information processing, thereby increasing speed while maintaining accuracy.
2Productivity
If statistical rules with threshold values are applied to automate CI identification, then productivity is improved by reducing manual intervention, but measurement precision may deteriorate due to automated rule-based matching
Solution Approach 1:
The system incorporates feedback mechanisms where the reconciliation engine continuously evaluates the performance of statistical rules and threshold values based on actual matching results. The engine adjusts and refines rules based on feedback from successful and unsuccessful matches, improving identification accuracy over time while maintaining automated high-speed processing.
Solution Approach 2:
The patent dynamically adjusts threshold values and rule parameters based on the specific characteristics of the data being processed. By changing parameters such as threshold sensitivity and rule weighting, the system optimizes the balance between automated processing speed and identification precision for different reconciliation scenarios.
3Manufacturing precision
If multiple predefined rules are evaluated in priority order to identify CIs, then manufacturing precision is improved by systematic matching, but device complexity increases due to multiple rule evaluations
Solution Approach 1:
The patent segments the reconciliation process into distinct phases: first evaluating high-priority rules with strict matching criteria, then progressively evaluating lower-priority rules with more flexible criteria. This segmentation allows the system to handle complex multi-rule evaluation systematically by breaking it into manageable stages, maintaining precision while organizing complexity.
Solution Approach 2:
The system performs preliminary evaluation of high-priority rules first to quickly identify obvious matches before proceeding to more complex rule evaluations. By handling simple cases upfront with preliminary actions, the system reduces the burden of subsequent complex evaluations, maintaining matching precision while managing overall process complexity.
Data Source
AI summary
A system for reconciling object for a configuration management databases employs statistical rules to reduce the amount of manual identification required by conventional reconciliation techniques. As users manually identify matches between source and target datasets, statistical rules are developed based on the criteria used for matching. Those statistical rules are then used for future matching. A threshold value is adjusted as the statistical rules are used, incrementing the threshold value when the rule successfully matches source and target objects. If the threshold value exceeds a predetermined acceptance value, the system may automatically accept a match made by a statistical rule. Otherwise, suggestions of possibly applicable rules may be presented to a user, who may use the suggested rules to match objects, causing adjustment of the threshold value associated with the suggested rules used.