Codeless Platform Data Processing via AI Feedback Loops
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Solution Overview
Problem
Enterprise applications developed on codeless platforms face challenges in managing complex functionalities due to dynamic data changes, inefficient data processing, and lack of visibility in process flows, leading to disrupted execution cycles and inefficient functioning.
Innovation Solution
A data processing system utilizing AI and machine learning to analyze application data, generate scenarios, and manage operations through a layered architecture with configurable components, enabling seamless and error-free execution of complex operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprise applications are developed on codeless platforms with dynamic attributes and random modifications in process flows, then application functionality and adaptability are improved, but defining prioritization rules for task management and maintaining system reliability becomes challenging
Solution Approach 1:
The system implements a feedback mechanism where the AI engine continuously monitors process flow changes, identifies affected tasks, and automatically updates prioritization rules. This closed-loop approach ensures that dynamic modifications maintain system reliability by adjusting task management based on real-time feedback about process changes.
Solution Approach 2:
The system dynamically changes prioritization parameters based on process flow modifications. When attributes or processes are modified, the AI engine recalculates task priorities using updated parameters, ensuring that task management remains reliable despite the dynamic nature of codeless platform applications.
2Device complexity
If legacy systems use separate components for accepting transaction objects without visibility into process flows, then system modularity is improved, but integration complexity and loss of information increase
Solution Approach 1:
The AI engine acts as an intermediary component that bridges the gap between separate system components and process flow context. It receives transaction objects, enriches them with process flow information from the codeless platform, and routes them appropriately, thereby reducing information loss while maintaining modularity.
Solution Approach 2:
The AI engine performs multiple functions including monitoring process flows, identifying task modifications, prioritizing tasks, and integrating with legacy systems. This multi-functional approach reduces the need for multiple specialized components, decreasing integration complexity while maintaining system modularity.
3Reliability
If RDBMS is used for transactional support in applications with dynamic data and changing flows, then data consistency is improved, but system flexibility and productivity decrease due to monolithic architecture
Solution Approach 1:
The system segments the monolithic RDBMS architecture by introducing an AI engine layer that handles dynamic data processing and task management separately from the core transactional database. This segmentation allows the RDBMS to maintain data consistency while the AI engine provides the flexibility needed for dynamic data flows, thereby improving overall productivity.
Solution Approach 2:
The AI engine serves as an intermediary between the dynamic application layer and the RDBMS, translating dynamic data requirements into structured database operations. This mediation maintains data consistency in the RDBMS while enabling flexible, high-productivity processing of dynamic data flows at the application level.
4Ease of manufacture
If reusable codes are used in codeless platforms, then ease of manufacture and adaptability are improved, but functionality is restricted due to underlying architecture limitations
Solution Approach 1:
The AI engine provides self-service capabilities that allow reusable code components to automatically adapt to new data processing requirements. It monitors for architectural limitations, identifies appropriate task modifications, and adjusts the execution of reusable codes dynamically, thereby extending their functionality without sacrificing ease of manufacture.
Solution Approach 2:
The system introduces dynamic behavior to reusable code components through the AI engine, which can modify task execution parameters, prioritize tasks differently, and adapt processing logic based on real-time conditions. This dynamic layer enables reusable codes to handle diverse data processing scenarios while maintaining their reusability and ease of deployment.
Data Source
AI summary
The present invention provides a data processing system and method for operating one or more enterprise applications developed by a codeless platform. The invention includes a layered platform architecture for supporting and executing data processing in enterprise applications. The data processing system and method provides generation of one or more scenarios by a bot utilizing a domain model structure and at least one application module for execution of one or more operations associated with the one or more scenarios.