Dynamic Process Prioritization Using Multi-Attribute Hashing
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Solution Overview
Problem
Conventional business process prioritization methods rely on static approaches that fail to consider dynamic run-time aspects and multiple attributes from various information domains, leading to inefficient prioritization and re-prioritization of business processes.
Innovation Solution
A cross-domain multi-attribute hashed and weighted dynamic process prioritization method that identifies and weights attributes from multiple informational domains, including business-level requirements, infrastructure capabilities, and historical performance, to assign dynamic priorities to process-level input requests, using hashing and weighting techniques to categorize and re-prioritize processes based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static priorities are assigned to processes, then the prioritization system is simple to implement, but it fails to consider dynamic run-time aspects and multiple attributes from various information domains
Solution Approach 1:
The patent implements dynamic prioritization by continuously monitoring multiple attributes from different information domains (business processes, infrastructure, historical performance) and adjusting process priorities in real-time based on current conditions, transforming the static prioritization system into a dynamic one that adapts to changing run-time aspects
Solution Approach 2:
The prioritization system is segmented into multiple independent attribute domains (business-level attributes, infrastructure attributes, historical performance attributes) that can be identified, weighted, and processed separately, then combined to form the overall dynamic priority, making the complex system manageable through modular decomposition
2Measurement precision
If multiple attributes from multiple informational domains are identified and weighted dynamically, then the prioritization accuracy and business requirement compliance improve, but the processing complexity and computational overhead increase
Solution Approach 1:
The system performs preliminary identification of relevant attributes from multiple information domains before the actual prioritization decision is made, pre-processing and organizing the multi-attribute data so that the final weighting and priority assignment can be executed efficiently with reduced computational overhead
Solution Approach 2:
The system dynamically changes the weights assigned to different attributes based on current business requirements and run-time conditions, allowing the prioritization model to adapt its parameters (attribute weights) to achieve higher accuracy in different operational contexts without requiring a completely new processing system
3Productivity
If dynamic prioritization is implemented using hashing and weighting techniques, then the re-prioritization efficiency improves, but the computational resources required increase
Solution Approach 1:
The system uses hashing techniques to create condensed representations (copies) of the multi-attribute priority data, allowing rapid re-prioritization decisions to be made by comparing and manipulating these hashed representations rather than processing the full multi-attribute datasets, thereby improving re-prioritization efficiency while reducing computational resource consumption
Data Source
AI summary
A set of attributes are identified within a received input request of a workflow process. The attributes at least in part represent historical process performance of similar workflow processes. Each of the attributes are weighted into a weighted process prioritization data set based upon the historical workflow process performance. The input request is assigned to a process priority based upon the weighted process prioritization data set.