Adaptive Engine for Geographic Data Standardization
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Solution Overview
Problem
The collection and management of geographical data from multiple vendors in varying formats pose challenges due to format inconsistencies and frequent changes, making it difficult to provide accurate and standardized data for software applications.
Innovation Solution
A system that utilizes a transformation component to process input data from multiple vendors into a standardized format using sets of rules, which can be updated separately from the transformation component, and incorporates artificial intelligence for error detection and rule generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If geographic data is collected from multiple vendors in various formats, then the coverage and quantity of geographic data increase, but the complexity of data processing and standardization increases
Solution Approach 1:
The patent introduces a transformation component as an intermediary between vendor-specific data formats and the standardized output format. This transformation component contains configurable transformation rules that mediate the conversion process, allowing data from multiple vendors to be standardized without requiring changes to the core processing system.
Solution Approach 2:
The patent segments the data processing system into distinct modular components: data collection modules for different vendors, a transformation component with configurable rules, and an output generation module. This segmentation allows each component to be independently developed, maintained, and updated, reducing overall system complexity.
2Reliability
If geographic data formats are frequently updated by vendors, then the data accuracy and relevance improve, but the difficulty of maintaining processing systems increases
Solution Approach 1:
The patent implements a dynamic transformation rule system where the transformation component can be reconfigured without modification of the core processing code. When vendor formats change, new transformation rules can be loaded into the existing transformation component, allowing the system to adapt to format changes while maintaining structural stability.
Solution Approach 2:
The transformation component uses configurable parameters and transformation rules that can be adjusted to accommodate changes in vendor data formats. By changing the transformation parameters rather than the underlying system architecture, the patent enables easy adaptation to format updates while maintaining system integrity.
3Productivity
If transformation rules are hard-coded into the processing system, then the processing speed improves, but the adaptability to format changes decreases
Solution Approach 1:
The patent pre-configures the transformation component with a library of transformation rules and templates that can be selected and applied based on the input data source. This preliminary preparation allows the system to quickly match incoming data to appropriate transformation rules without requiring complex runtime analysis, maintaining processing speed while enabling adaptability.
Solution Approach 2:
The transformation component is designed as a universal, multi-functional module that can handle multiple vendor formats through a single configurable interface. Rather than having separate hard-coded processing paths for each vendor, the universal transformation component can be configured to work with any vendor format, providing both speed and adaptability.
Data Source
AI summary
The subject disclosure pertains to systems and methods for processing input data provided in multiple formats to generate output data in a standardized, common format. To facilitate processing of the input data, a set of rules can be defined that describe processing for various types and formats of input data. The rules can be maintained separately from the processing component, such that the rules can be updated without necessitating modification of the processing component. Subsets of the rules can be retrieved and utilized to process specific input data. Errors within the input data can be identified and automatically corrected based upon previous input data sets. In addition, rules can be automatically generated based on previous input data and user feedback.