Data-Driven Execution System Automating Complex Enterprise Processes
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Solution Overview
Problem
Conventional systems lack the capability to automatically process complex enterprise processes and fail to flexibly meet diverse business requirements due to scattered data and business logic, relying heavily on user knowledge and experience.
Innovation Solution
A data-driven execution system that includes a processor and storage device, which builds a data graph and generates a recommended plan based on task paths to automate business process execution, allowing for flexible and adaptive business logic processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional process engines or ERP systems are used, then business processes can be executed, but the system lacks the bearing capacity for knowledge and cannot automatically handle complex enterprise processes
Solution Approach 1:
The system segments complex enterprise processes into modular task paths within a data graph structure. Each node represents a discrete task or decision point, allowing the system to break down overwhelming complexity into manageable, searchable units that can be automatically processed
Solution Approach 2:
The patent introduces a data graph as an intermediary layer between scattered business data and the execution engine. This data graph serves as a knowledge repository that mediates between raw data and automated decision-making, enabling the system to handle complexity without requiring direct human intervention in every decision
2Adaptability or versatility
If conventional systems with scattered data and business logic are used, then existing processes can be maintained, but the system cannot flexibly meet diverse business requirements
Solution Approach 1:
The data graph structure serves multiple functions simultaneously: it stores business data, defines process logic, enables searching for task paths, and supports automated plan generation. This universal structure eliminates the need for separate systems for data management and process execution, providing flexibility without proportional increases in system complexity
Solution Approach 2:
The system dynamically adapts to different business requirements by searching the data graph for relevant task paths based on specific data processing requests. Rather than requiring rigid pre-programming for every scenario, the system dynamically retrieves and executes appropriate task sequences, enabling flexible adaptation to diverse business needs
3Productivity
If conventional systems rely on user knowledge and experience, then complex processes can be handled with human judgment, but manual operations remain necessary and efficiency is reduced
Solution Approach 1:
The system performs self-service by automatically generating execution plans through searching the data graph and identifying appropriate task paths. Rather than requiring users to manually process each business request, the system autonomously retrieves relevant tasks and generates executable plans, significantly improving productivity while reducing manual operational requirements
Data Source
AI summary
A data-driven execution system and an execution method thereof are provided. The data-driven execution system includes a storage device and a processor. The storage device is used for storing multiple modules. The processor is coupled to the storage device and used to execute multiple modules. The processor receives a data processing request. The processor searches a data graph according to the data processing request to obtain at least one task path. The processor generates a recommended plan according to one of the at least one task path.