Interactive Scheduling Analysis for Holistic Multi-Source Evaluation
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Solution Overview
Problem
Existing data systems struggle to efficiently and holistically evaluate the effects of rescheduling options across disparate data stores and formats, leading to an unfeasible and inefficient rescheduling process due to the complexity of variables and their interactions.
Innovation Solution
A data analysis system that receives data from a master data system, incorporating sophisticated data analysis and interactive graphical user interfaces to efficiently evaluate and suggest rescheduling options, considering various metrics, and implements these changes back into the master data system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from disparate data stores and databases are combined and evaluated to enable holistic evaluation of rescheduling options, then the completeness and accuracy of rescheduling evaluation is improved, but the system complexity and computational burden increase exponentially
Solution Approach 1:
The patent segments the rescheduling evaluation process into distinct modules: data collection from multiple sources, data normalization and integration, rescheduling option generation, impact analysis, and recommendation presentation. This segmentation allows each module to handle specific tasks independently, reducing the exponential complexity that would arise from attempting to process all data and variables in a single monolithic system.
Solution Approach 2:
The patent introduces an intermediary data analysis system that acts as a mediator between the disparate data stores and the rescheduling evaluation process. This intermediary system collects, normalizes, and integrates data from multiple sources before presenting it to the rescheduling algorithm, thereby simplifying the overall system architecture and reducing computational burden while maintaining holistic evaluation accuracy.
2Reliability
If all variables and their interactions are considered when evaluating rescheduling options, then the comprehensiveness of the evaluation is improved, but the computational feasibility deteriorates
Solution Approach 1:
The patent applies local quality by prioritizing the analysis of variables and interactions based on their local importance to specific rescheduling decisions. Rather than treating all variables equally, the system identifies and focuses computational resources on the most critical variables and their interactions, thereby maintaining evaluation comprehensiveness while improving computational efficiency.
Solution Approach 2:
The patent implements partial action by analyzing a subset of the most influential variables and their interactions first, rather than attempting to evaluate all possible variables simultaneously. This approach allows the system to provide comprehensive evaluations within reasonable computational timeframes by focusing on the most impactful factors while still considering additional variables as needed.
3Loss of time
If time-sensitive data is processed more frequently to provide up-to-date information for rescheduling, then the timeliness of rescheduling decisions is improved, but the resource consumption increases
Solution Approach 1:
The patent implements periodic action by processing time-sensitive data at scheduled intervals rather than continuously. The system determines appropriate processing frequencies based on the time-sensitivity of different data types, processing critical data more frequently and less critical data less frequently, thereby maintaining data freshness while optimizing resource consumption.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the processing frequency of different data types based on their time-sensitivity characteristics. Critical time-sensitive data is processed more frequently with higher priority, while less time-sensitive data is processed at lower frequencies, allowing the system to maintain up-to-date information for rescheduling decisions while managing computational resources efficiently.
Data Source
AI summary
A data analysis system is disclosed that receives data from a master data system to enable useful and efficient rescheduling of items, taking into account effects of various rescheduling options on various metrics related to the items and/or the scheduling. The data analysis system includes sophisticated data analysis and interactive graphical user interface functionality to enable efficient, multi-variable evaluation of various rescheduling options. The interactive graphical user interface includes interactive functionality for suggesting rescheduling options in view of the effects of those changes on various metrics, evaluating various rescheduling options in view of effects on the various metrics, adjusting instances of metrics related to items/timelines in view of scheduling changes, and the like. Once a set of schedule modifications are determined by the data analysis system, the data analysis system can push the schedule modifications back to the master data system for implementation.