Dynamic Rule Orchestration for Big Data Context Adaptation
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Solution Overview
Problem
Traditional methods for deriving business rules in big data analytics are not dynamic and fail to adapt to real-time changes and context, requiring manual intervention and lacking automation in rule orchestration.
Innovation Solution
A method and system for dynamic orchestration of rules in a big data environment that continuously monitors activities, detects events, determines scenarios, correlates them with dimensions, and applies operational constraints to migrate and update rules in real-time across target systems such as kiosks or POS systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional predefined methods are used to derive business rules, then rule stability is maintained, but adaptability to dynamic business requirements deteriorates
Solution Approach 1:
The patent implements dynamic rule configuration by enabling real-time monitoring of business activities and automatic derivation of new rules based on detected events and scenarios. The system transitions from static predefined rules to dynamic rules that automatically adapt to changing business requirements through continuous data analysis and pattern recognition.
Solution Approach 2:
The system performs self-service by automatically deriving, validating, and deploying business rules without requiring manual developer intervention. The rule engine autonomously monitors activities, detects events, determines scenarios, and generates appropriate rules based on predefined policies and dimensional analysis, eliminating the need for manual rule configuration.
2Productivity
If manual intervention is used for rule orchestration, then rule accuracy is maintained, but productivity deteriorates
Solution Approach 1:
The patent implements feedback mechanisms through validation processes that verify derived rules against predefined criteria and dimensional constraints. The system continuously monitors rule performance and business activities, using this feedback to refine and adjust rules automatically, ensuring both speed and accuracy in rule orchestration.
Solution Approach 2:
The system performs preliminary validation and verification of derived rules before deployment by checking them against predefined policies, dimensional constraints, and business requirements. This preliminary action ensures rule accuracy is maintained while enabling automated rapid deployment, as rules are pre-validated before being applied to business operations.
3Adaptability or versatility
If real-time rule updates are implemented, then adaptability improves, but system complexity deteriorates
Solution Approach 1:
The patent segments the rule management system into distinct functional modules: activity monitoring, event detection, scenario determination, rule derivation, validation, and deployment. Each module handles a specific aspect of the process, reducing overall system complexity by dividing the complex real-time rule adaptation task into manageable, specialized components.
Solution Approach 2:
The system introduces intermediary validation layers and dimensional analysis frameworks that mediate between raw business data and derived rules. These intermediaries structure and filter information before rule derivation, simplifying the complexity of real-time analysis by providing organized intermediate representations that guide rule generation.
Data Source
AI summary
The present disclosure relates to a method and system for performing dynamic orchestration of rules. The system monitors activities performed by entity in the big data environment to detect events. The events are associated with product/service. Further, the system determines scenario by analyzing data pertaining to the product or the service. The scenario comprises one or more scenario categories. Further, the scenario is correlated with the events based on the one or more scenario categories. The correlation is further validated by the system based on dimensions. Further, the system derives one or more rules for each of the correlation of the scenario and the events upon validation. The system may further apply an operational constraints and migration controls to the one or more rules to perform dynamic orchestration. Thus, the system provides one-stop solution for deriving the rules based on context of the scenarios and migrating them to target systems.