Maintenance-Aware Performance Reporting Without Full Reanalysis
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Solution Overview
Problem
Performance management systems often fail to accurately account for maintenance activities in real-time reporting due to unrecorded or erroneously scheduled maintenance operations, leading to inaccurate performance metrics and inefficient recalibration processes.
Innovation Solution
A maintenance-aware reporting system that maintains metadata in intermediate time-based datasets to identify and adjust performance insights for unreported maintenance activities without reanalyzing the entire performance data, ensuring accurate reporting by configuring maintenance windows at a later time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recalibration is performed to account for maintenance operations, then reporting accuracy is improved, but time consumption and computational resources increase
Solution Approach 1:
The system pre-processes performance data and stores it in an intermediate data structure that maintains granularity of metadata and temporal relationships. This preliminary organization allows maintenance operations to be accounted for by simply adjusting the pre-processed data rather than reanalyzing raw data, significantly reducing the time required for recalibration while maintaining reporting accuracy.
Solution Approach 2:
The patent introduces an intermediate data structure that serves as a mediator between raw performance data and final reports. This intermediate representation preserves essential metadata and temporal information, enabling efficient adjustments when maintenance operations are identified without requiring full recalibration of the original data, thus resolving the contradiction between accuracy and time consumption.
2Measurement precision
If recalibration is performed to add missing maintenance operations, then reporting accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary processing of performance data to create an intermediate structure that maintains metadata granularity and temporal context. When missing maintenance operations are identified, the system can efficiently adjust this pre-processed data structure rather than reanalyzing all raw data, reducing computational complexity while ensuring reporting accuracy.
Solution Approach 2:
The patent segments the performance data processing into distinct stages: initial pre-processing to create intermediate structures, identification of maintenance operations, and adjustment of affected data. This segmentation allows the system to focus computational resources only on the portions of data affected by maintenance operations rather than recalculating the entire dataset, thereby reducing overall computational complexity.
3Productivity
If real-time reporting is maintained, then reporting speed is improved, but accuracy decreases when maintenance operations are unrecorded
Solution Approach 1:
The system performs preliminary processing of performance data in real-time, organizing it into an intermediate structure that preserves metadata and temporal relationships. This allows the system to generate reports quickly from pre-processed data while maintaining the capability to identify and adjust for unrecorded maintenance operations, thus preserving both reporting speed and accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors performance data and maintenance operation records. When discrepancies are detected (such as unrecorded maintenance operations), the system can adjust the intermediate data structure and regenerate affected reports, ensuring accuracy is maintained without sacrificing real-time reporting capabilities.
Data Source
AI summary
Systems, apparatuses, and methods for maintenance aware performance reporting are disclosed. Responsive to a request for a performance report of devices and/or services for a given time range, reportable data is computed. Checks are performed to identify any maintenance windows that need to be configured in that time range. If such windows are to be configured, the performance insights for the entity are modified, without requiring to perform a reanalysis of their entire performance data. This is performed by utilizing metadata defining the performance insights, that are maintained along with performance stored in individual time-based datasets. Based on these modifications, updated performance reports can be created on-the-fly accounting for previously scheduled but unreported events. Further, the update is performed without reanalysis of original data, thereby maintaining efficiency and accuracy of the reports.