Data Connector for External Labor Management Systems
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Solution Overview
Problem
Conventional techniques fail to effectively convert data from external labor management systems into a format usable by internal systems, leading to incomplete real-time monitoring and decision-making in workplaces, which hinders risk mitigation and operational optimization.
Innovation Solution
A method and system that involve receiving user selections, determining data requirements, retrieving data from external systems, converting and validating the data to meet primary requirements, and outputting it to a data model for use by internal systems, enabling the integration and utilization of external data for real-time monitoring and risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is retrieved from external labor management systems, then the quantity and completeness of data for real-time monitoring is improved, but the data compatibility and usability by internal systems deteriorates due to format incompatibility
Solution Approach 1:
The patent implements a data connector that serves as an intermediary component between external labor management systems and internal systems. This connector retrieves data from external sources in various formats and transforms it into a standardized internal format, enabling seamless integration without requiring changes to either the external systems or the internal system core logic.
Solution Approach 2:
The system dynamically changes data parameters including format, structure, and validation rules based on the source system type. The data connector adapts retrieval parameters, transformation rules, and validation thresholds according to the specific external system being integrated, allowing the same internal system to work with multiple different external data sources.
2Reliability
If data conversion and validation processes are implemented, then the quality and reliability of data for decision-making is improved, but the complexity of the data processing system increases
Solution Approach 1:
The data processing system is segmented into distinct modular components: a data retrieval module that collects data from external systems, a transformation module that converts data formats and structures, and a validation module that verifies data quality against defined criteria. Each module handles a specific aspect of data processing, making the overall complex process manageable and maintainable through clear separation of concerns.
Solution Approach 2:
The data connector implements self-service capabilities by automatically adapting to different external system formats and structures. The system autonomously retrieves appropriate transformation rules based on the source system identification, performs format conversion, and validates data without requiring manual configuration for each data source, reducing operational complexity.
3Speed
If real-time data processing is implemented, then the speed of risk identification and mitigation is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary actions by pre-defining validation rules, data transformation templates, and risk threshold criteria before data processing begins. Data quality standards and conversion mappings are established in advance, allowing the real-time processing to focus only on applying these pre-configured rules rather than creating them during processing, thus reducing computational overhead during critical real-time operations.
Data Source
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AI summary
Disclosed are methods and systems for converting data from an external system to data to be used by an internal system. For instance, a method may include receiving at least one user selection from a list of domains for executing at least one task, determining one or more data requirements corresponding to the at least one user selection, in response to determining the one or more data requirements, retrieving data from one or more external systems, converting the retrieved data based on the one or more data requirements, the converting including extracting data from the retrieved data based on the one or more data requirements, validating the extracted data, the validating including determining that the extracted data meets or exceeds a primary data requirement threshold, and outputting the validated extracted data to a data model for use by an internal system to execute the at least one task.