Food Product Impact Indicators Through Multi-Database Data Merging
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Solution Overview
Problem
Existing software technologies are inefficient in determining environmental impact indicators across multiple products, especially when data is spread across multiple database tables, and lack the capability to process this data simultaneously for large-scale product sets.
Innovation Solution
A computer-implemented method for merging datasets from multiple database tables to determine environmental-impact indicators, including product-level and resource-level indicators, using key-based merging and additional columns for dry mass and distance calculations, and presenting visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing software technologies process environmental impact data from multiple database tables, then data completeness is improved, but processing efficiency deteriorates and scalability is limited
Solution Approach 1:
The patent merges multiple database tables (product-level ingredients, food products, and environmental impact values) into a unified processing framework that can handle large-scale datasets simultaneously. The system combines data from multiple sources through integrated merging operations, enabling efficient processing while maintaining data completeness across all product-level ingredients and their corresponding environmental impact indicators.
2Productivity
If the system processes data for large sets of products simultaneously, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent segments the environmental impact assessment process into distinct computational steps: extracting data from multiple database tables, merging the extracted datasets, calculating environmental impact indicators for each product-level ingredient, and generating visualizations. This segmentation allows the system to handle large-scale product datasets efficiently by breaking down the complex processing into manageable, sequential operations that can be executed simultaneously across multiple products.
3Measurement precision
If the system calculates environmental impact indicators for all product-level ingredients, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary data extraction and merging operations before calculating environmental impact indicators. By pre-processing the data structure and preparing the datasets in advance, the system optimizes the subsequent calculation process, enabling accurate environmental impact indicator determination for all product-level ingredients while minimizing processing time through efficient data preparation and structured computation.
Data Source
AI summary
A computing platform is configured to (1) extract first and second source datasets from a first database containing data about food products and a second database containing data about manufacturing processes, respectively, (2) merge the first and second source datasets into a first merged dataset, (3) generate an updated dataset including (i) rows representing data records for a set of product-level resources and (ii) columns representing data variables that provide information about the set of product-level resources, (4) extract third, fourth, and fifth source datasets from a third database containing data about resource types, a fourth source database containing data about manufacturing plants, and a fifth source database containing environmental-impact values for types of resources, respectively, (6) merge the updated dataset and the third, fourth, and fifth source datasets into a second merged dataset, and (7) determine a group of environmental-impact indicators for each product-level resource in the set.