Semantic Data Transformation Flow Automation
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Solution Overview
Problem
Current data transformation processes in IoT applications are time-consuming, tedious, and difficult to monitor and maintain, especially in dynamic environments where data sets continuously change, necessitating an automated management solution based on semantics.
Innovation Solution
The implementation of automated management of data transformation flows using a processor that creates and manages data transformation templates according to concepts, instances of concepts, relationships between concepts, and mappings to data sources, allowing for user-defined data transformations and automatic adaptation to changes in data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data transformation processes are used, then flexibility in handling diverse data sources is maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables self-service through automated detection of data source changes and automatic generation of transformation flows. When new data sources are detected, the system automatically creates transformation templates and updates existing flows without requiring manual intervention, thereby improving productivity while reducing management complexity
Solution Approach 2:
The system changes parameters by dynamically adapting transformation flows based on detected changes in data sources. It automatically modifies transformation parameters, data types, and flow configurations to match new data source characteristics, enabling efficient automated management of diverse data sources
2Productivity
If data transformation flows are manually configured for each data source, then customization accuracy is maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-defining transformation templates and configurations before actual data processing occurs. When new data sources are detected, the system automatically selects and applies pre-configured templates, eliminating the need for manual configuration and significantly reducing setup time
Solution Approach 2:
The system uses copying by replicating proven transformation templates to new data sources. Instead of manually configuring each transformation from scratch, the system copies existing successful transformation patterns and adapts them to new data sources, dramatically reducing configuration time while maintaining accuracy
3Adaptability or versatility
If data sources are dynamically added to the system, then system adaptability is improved, but monitoring and maintenance difficulty increase
Solution Approach 1:
The system implements feedback mechanisms that automatically detect changes in data sources and trigger corresponding transformations. When new data sources are added or existing ones change, the system receives feedback about these changes and automatically updates transformation flows, maintaining adaptability while simplifying monitoring through automated detection and response
Data Source
AI summary
Various embodiments are provided for intelligent management of data flows in a computing environment by a processor. One or more data transformation in time-series data applications templates may be created and managed according to concepts, one or more instances of the concepts, relationships between the concepts, and a mapping of the concepts to one or more data sources.