Manufacturing Data Exposition via Metamodel-Driven Zero-Downtime Updates
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Solution Overview
Problem
Existing methods for exposing manufacturing operation data from data warehouses require manual intervention and downtime for updating data models, leading to inefficiencies and increased costs due to error-prone processes.
Innovation Solution
A method and system for dynamic data exposition using a metamodel to automatically generate and deploy controllers and artifacts, enabling users to modify data models without direct database access, utilizing zero-downtime deployment techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to create and deploy controllers for data exposition, then data security and control are maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The system enables self-service through automated controller generation from metamodels. The compilation and deployment processes are performed automatically without manual intervention, allowing the system to generate and deploy controllers autonomously based on defined metamodel specifications, thereby eliminating human errors and reducing update time
Solution Approach 2:
The metamodels are created in advance as templates that define the structure and behavior of controllers. By preparing these metamodels beforehand, the system can rapidly generate controllers when needed without manual coding, significantly reducing the time required for updates while maintaining consistency and reducing errors
2Adaptability or versatility
If direct database exposition is implemented, then data access flexibility is improved, but data security concerns arise
Solution Approach 1:
Controllers act as intermediaries between the data warehouse and end users. These controllers expose data through standardized interfaces (such as OData) without allowing direct database access. This intermediary layer maintains data security by controlling and validating all data access operations while still providing flexible data access capabilities to users
3Productivity
If service continuity is maintained during model modifications, then user productivity is preserved, but deployment complexity increases
Solution Approach 1:
The deployment process is segmented into distinct phases: metamodel definition, automated controller generation, compilation, and deployment. This segmentation allows different components to be developed and tested independently, and enables continuous deployment strategies where new controllers can be deployed without taking the entire system offline, thus maintaining service continuity while managing complexity through structured processes
Data Source
AI summary
A method and a system perform dynamic exposition of data from a data repository. The method includes the steps of: providing one or more data model entities relevant to the data to be exposed; creating a controller for the or each data model entity; compiling the or each controller thereby creating a respective artifact; and processing the artifact(s) to obtain the data exposition at a data exposition host. The step of providing at least one data model entity includes creating a logical, human-readable representation of the data model (e.g. a metamodel), and the steps of creating and compiling the or each controller and creating and processing the artifact(s) are automatically performed starting from the metamodel(s) when a user modifies the latter and requests data exposition.