Inference Prediction Graphs for Data Processing Plan Management
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Solution Overview
Problem
Managing complex data processing systems with unreliable inference model predictions and high variability leads to inefficient policy definition and increased computational resources, making it challenging to maintain desired computer-implemented services.
Innovation Solution
Utilizing an acyclic graph to represent predicted future states and their statistical characterizations, allowing for proactive plan management by clustering and visually interpreting large volumes of complex data, thereby reducing computational resources and improving service reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference model predictions are used to manage data processing systems, then proactive plan management is enabled, but high variability and unreliability of predictions lead to inefficient policy definition and increased computational resources
Solution Approach 1:
An acyclic graph is introduced as an intermediary data structure to represent predicted future states and their statistical characterizations. This graph serves as a mediator between the inference model predictions and the policy definition process, organizing the complex prediction data into a manageable format that reduces computational overhead while maintaining reliability.
Solution Approach 2:
The patent segments the complex prediction data by clustering predicted future states into groups based on their statistical characterizations. This segmentation transforms the overwhelming volume of raw predictions into discrete, manageable clusters that can be processed more efficiently, reducing the computational resources required while preserving the essential information for reliable service management.
2Loss of information
If large volumes of prediction data are analyzed, then comprehensive plan management is achieved, but computational resources increase and interpretability decreases
Solution Approach 1:
Multiple predicted future states with similar statistical characterizations are merged into clustered groups within the acyclic graph. This merging process consolidates large volumes of individual predictions into fewer representative clusters, making the data more interpretable and easier to operate with while retaining the essential information from the original predictions.
Solution Approach 2:
The patent transforms the high-dimensional prediction data into a different dimensional representation using the acyclic graph structure. By organizing predictions along dimensions of statistical characterization and temporal progression rather than individual data points, the system achieves better interpretability without losing information, allowing operators to navigate and understand the prediction space more effectively.
3Reliability
If frequent policy updates are made based on predictions, then service reliability improves, but computational overhead and operational complexity increase
Solution Approach 1:
The system performs preliminary organization of prediction data into the acyclic graph structure and pre-clusters predicted states before policy definition is required. This preliminary action prepares the prediction data in advance, so when policies need to be updated, the system can quickly reference the pre-organized clusters rather than processing raw predictions from scratch, reducing policy definition time while maintaining frequent updates for reliability.
Solution Approach 2:
The patent changes the parameters used for policy management from individual prediction instances to aggregated statistical characterizations of prediction clusters. This parameter change allows policies to be defined and updated based on summary statistics rather than exhaustive analysis of each prediction, significantly reducing the time required for policy updates while maintaining the ability to respond frequently to changing conditions.
Data Source
AI summary
Methods and systems for managing operation of a data processing system are disclosed. To do so, potential future operations of the data processing system may be displayed to a viewer via a graphical user interface. User input may be obtained based on the potential future operations and a potential plan may be obtained based on the user input. The potential plan may include one or more actions to be performed at a point in time and limitations for enforcement of the potential plan. An impact of the potential plan may be simulated using acyclic graphs and the user may determine, based on the acyclic graphs, whether the potential plan is acceptable. The potential plan may be used as a plan for the data processing system and the limitations may be based on a statistical characterization of the potential future operations and a likelihood of an outcome for the plan occurring.