Semantic Data Exchange Module for Collaborative Engineering Tools
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Solution Overview
Problem
In collaborative engineering environments, data exchange between applications with different data formats leads to redundancy and synchronization issues, causing shared data to become out of sync, as each tool has its own formatting scheme that is not readable by others.
Innovation Solution
A system and method for data exchange that includes a data module to receive and convert data between applications using a semantic language format, such as RDF or OWL, and a synchronization module to ensure changes are replicated across applications, using APIs and query languages like SPARQL for access and synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If data is exchanged between applications with different data formats, then data redundancy is eliminated, but shared data gets out of sync
Solution Approach 1:
The patent introduces an intermediary conversion module that translates data between different application formats. This mediator converts source application data into target application format, enabling data exchange while maintaining synchronization. The conversion module acts as a bridge that prevents data desynchronization by ensuring consistent data transformation across applications.
Solution Approach 2:
The patent changes the data format parameters through automated conversion processes. By dynamically transforming data between different formats (e.g., JSON, XML, CSV) based on the target application's requirements, the system eliminates redundancy while maintaining synchronization. The parameter changes are managed through conversion rules that ensure data consistency.
2Adaptability or versatility
If each tool uses its own formatting scheme, then tool-specific requirements are met, but data readability across tools is poor
Solution Approach 1:
The conversion module is designed with multi-functionality to handle multiple data formats and conversion scenarios. It can convert between various formats (JSON, XML, CSV, YAML) and adapt to different application requirements. This universal converter maintains tool-specific formatting needs while ensuring cross-tool readability through standardized conversion processes.
Solution Approach 2:
The conversion module serves as an intermediary that translates between different tool-specific formats. It receives data in one format, processes it through conversion rules, and outputs data in the target format required by the receiving application, thereby maintaining both tool-specific requirements and cross-tool compatibility.
3Reliability
If manual data synchronization is performed, then data consistency can be maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by establishing conversion rules and synchronization protocols in advance. Conversion rules are pre-configured to define how data should be transformed between formats, and synchronization mechanisms are set up beforehand to automatically execute when data changes occur. This eliminates the need for manual intervention and reduces synchronization time.
Solution Approach 2:
The synchronization module implements feedback mechanisms that automatically detect data changes and trigger conversion processes. When source data changes, the system receives feedback about the change, automatically initiates conversion to the target format, and updates the target application. This closed-loop feedback system maintains data consistency without manual intervention.
4Productivity
If automated conversion is implemented, then synchronization speed improves, but system complexity increases
Solution Approach 1:
The conversion system is segmented into distinct modular components: a conversion module for format transformation, a synchronization module for coordinating data exchange, and a rule engine for managing conversion logic. Each module has a specific function and can be independently configured and maintained. This segmentation reduces overall system complexity by making each component manageable and interchangeable.
Solution Approach 2:
The system manages complexity through parameterized conversion rules that can be configured without changing the underlying system architecture. Conversion parameters (formats, data types, transformation logic) are defined as configurable parameters rather than hard-coded complexity, allowing the system to adapt to different scenarios while maintaining a consistent structural framework.
Data Source
AI summary
An apparatus, system, method, and program product are disclosed for data exchange in a collaborative environment. A data module receives first data from a first application that corresponds to second data of a second application. The first data has a structured format readable by the first application, but not readable by the second application. The structured format includes a semantic language format. A conversion module converts the first data from the semantic language format to an intermediate data having a format readable by the second application. A synchronization module synchronizes the intermediate data with the second data such that changes made to the first data by the first application are replicated in the second data of the second application.