Stochastic Workflow Replacement for Data Set Processing
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Solution Overview
Problem
Collaborative data set workflows experience prolonged waiting periods and access delays due to review interventions, leading to increased network traffic and complexity in associating data sets with appropriate workflows, which can be error-prone and resource-intensive.
Innovation Solution
Implementing a stochastic workflow replacement mechanism that determines whether to assign a modified workflow to a data set based on predefined start conditions, reducing access delays and network traffic by automating status information manipulation and simplifying workflow definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a data set is subjected to review intervention in a collaborative workflow, then data integrity is ensured, but access delays occur and processing time increases
Solution Approach 1:
The patent applies preliminary action by implementing automated review mechanisms that pre-validate data sets against defined criteria before they reach human reviewers. This preliminary automated checking ensures basic data integrity requirements are met beforehand, reducing the burden on manual review processes and allowing data sets that pass automated checks to proceed with minimal delay.
Solution Approach 2:
The review process is segmented into multiple independent stages: automated validation, conditional routing, and selective human review. Only data sets that fail automated checks or meet specific risk criteria are routed to human reviewers, while others proceed through accelerated paths. This segmentation allows most data to maintain integrity through automation while minimizing access delays for low-risk items.
2Manufacturing precision
If complex start conditions are specified to determine the appropriate workflow for a data set, then workflow accuracy is improved, but system complexity increases and error-proneness increases
Solution Approach 1:
The patent implements dynamic workflow selection where the workflow assignment is not determined by static complex conditions but by real-time evaluation of data set characteristics and current system state. The system dynamically adjusts workflow routing based on data type, urgency, reviewer availability, and historical performance metrics, simplifying the decision logic while maintaining high accuracy through adaptive rather than rigid condition checking.
Solution Approach 2:
An intermediary intelligent routing layer is introduced between data set submission and workflow assignment. This intermediary component uses machine learning models and rule-based systems to evaluate data sets and recommend appropriate workflows, reducing the complexity of direct condition-based routing while improving accuracy through pattern recognition and historical analysis.
3Manufacturing precision
If review intervention is implemented for data sets, then quality control is maintained, but network traffic increases and resource occupancy increases
Solution Approach 1:
The patent extracts the quality control function from centralized human review processes and distributes it through automated validation systems deployed at multiple points in the workflow. Local automated validators at data sources and intermediate processing points perform initial quality checks, extracting only data sets that fail validation or require human judgment from the main review pipeline, thereby reducing network traffic and resource occupancy while maintaining quality control.
4Reliability
If data sets are made inaccessible during review intervention to ensure data integrity, then data consistency is maintained, but productivity of other users decreases
Solution Approach 1:
The patent implements local quality control by making only the specific data fields under review inaccessible during review intervention, rather than locking entire data sets. Other fields and related data sets that do not depend on the reviewed data remain accessible to other users. This selective locking approach maintains data consistency for critical fields while allowing continued productivity for users working on independent data elements.
Data Source
AI summary
A system and method for the resource-saving collaborative handling of data sets in a computer network is described. The method comprises the steps of providing a data set having an initial collaborative workflow, determining if the data set satisfies a set of one or more first start conditions to trigger a random function query, providing the data set to a random function if the random function query is triggered, stochastically determining, by the random function, whether or not to assign a modified collaborative workflow to the data set, and replacing the initial workflow with the modified workflow for the data set if the random function determines to assign the modified workflow.