Semantic Data Mapping With User-Led Label Propagation
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Solution Overview
Problem
Existing data mapping technologies require manual intervention by programmers or data experts, limiting scalability and flexibility, especially when dealing with large datasets and non-technical users who need to annotate data with contextual and semantic meanings relevant to their analysis.
Innovation Solution
A data mapping system that generates a list of datapoints to be mapped, receives user input for new labels, and automatically maps additional datapoints, using machine learning algorithms to introduce vernacular and contextual meaning, enabling scalable and customizable data labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data mapping is performed by programmers or data experts, then mapping accuracy and reliability are improved, but scalability and productivity deteriorate
Solution Approach 1:
The system enables non-technical users to perform data mapping independently through an automated platform that provides mapping recommendations and allows direct input of mapping rules, eliminating the need for programmer intervention while maintaining mapping quality through user-friendly interfaces and automated validation
Solution Approach 2:
The patent replaces the mechanical process of manual coding and expert intervention with an automated computational system that uses machine learning algorithms to generate mapping recommendations and automatically applies mapping rules, transforming a labor-intensive process into an automated information processing system
2Measurement precision
If manual data mapping is performed by experts, then mapping precision and contextual understanding are improved, but time consumption and resource requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing data to identify potential mappings and generating mapping recommendations before the user finalizes the mapping, reducing the time required for users to perform mapping tasks while maintaining precision through pre-computed analysis
Solution Approach 2:
The system incorporates feedback mechanisms where mapping results are continuously refined based on user corrections and system learning, improving mapping precision over time while reducing the time required for iterative adjustments through automated suggestion updates
3Productivity
If automated mapping algorithms are used, then productivity and scalability are improved, but adaptability to domain-specific contextual meanings deteriorate
Solution Approach 1:
The system acts as an intermediary between automated algorithms and domain expertise by allowing non-technical users to provide contextual feedback and adjust mapping recommendations, combining the scalability of automation with the adaptability of human domain knowledge through a collaborative interface
Solution Approach 2:
The patent creates a universal platform that serves multiple functions: automated mapping generation, user feedback integration, contextual adaptation, and scalable deployment across different domains, allowing the same system to adapt to various domain-specific requirements while maintaining productivity
Data Source
AI summary
A method for mapping data by a data mapping system, comprising: generating, from a database of datapoints, a list of one or more datapoints to be mapped, wherein the list of one or more datapoints is saved in a datapoints results table; providing the list of one or more datapoints to be mapped; receiving a mapping input comprising a new mapping of one or more of the datapoints, the mapping comprising a new label for a datapoint; automatically mapping, based on the received mapping input, one or more additional datapoints from the database of datapoints; and automatically saving the new mapping of one or more of the datapoints and the mapped one or more additional datapoints from the database of datapoints in a results database.