Food Record Scoring and Clustering for Database Standardization
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Solution Overview
Problem
The challenge lies in processing and standardizing user-entered food descriptions, which often vary in format, making it difficult to utilize and manage nutritional data effectively, leading to inconsistencies and inefficiencies in data retrieval and storage.
Innovation Solution
A system comprising a database, processors, and a food data processing engine that determines a score for food records based on their frequency of logging and search results, standardizes and normalizes descriptions, and clusters similar records to designate candidate records, ensuring consistent data formatting and efficient retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user-entered food descriptions are stored in various formats without standardization, then user flexibility and ease of data entry are improved, but data consistency and retrieval efficiency deteriorate
Solution Approach 1:
The system performs preliminary standardization actions by calculating scores for food records based on logging frequency and search appearance, then automatically designates representative records before data retrieval operations occur. This pre-processing ensures data consistency is established in advance, resolving the contradiction between ease of entry and data reliability.
Solution Approach 2:
The system enables self-service standardization by automatically clustering similar food records and designating representatives without requiring manual intervention. The scoring mechanism and automatic clustering allow the system to self-regulate data consistency while maintaining user flexibility in entry formats.
2Reliability
If manual modification of individual user entries is performed to conform to desired data format, then data consistency is improved, but processing time and operational complexity increase
Solution Approach 1:
The system replaces manual modification with automated self-service processing. By calculating scores based on logging frequency and search appearance, and automatically designating representative records, the system achieves data consistency without human intervention, eliminating the time loss associated with manual formatting.
Solution Approach 2:
The system changes the parameter of data standardization from manual format conversion to automated score-based selection. By transforming the approach from modifying individual entries to selecting representatives based on frequency and relevance parameters, the system maintains consistency while reducing processing time.
3Productivity
If automated scoring and clustering of food records is implemented, then data standardization and retrieval efficiency are improved, but system complexity increases
Solution Approach 1:
The system segments the food record database into clusters based on similarity, with each cluster having a designated representative record. This segmentation approach improves retrieval efficiency by organizing data into manageable groups while keeping the scoring and clustering logic modular, thus managing system complexity.
Solution Approach 2:
The scoring mechanism serves multiple functions: it evaluates food record quality, determines cluster representatives, and guides data standardization. This multi-functionality improves productivity by using a single scoring system for multiple purposes rather than requiring separate mechanisms for each function.
Data Source
AI summary
Disclosed embodiments include apparatuses, methods and storage media associated with modifying a food record database. The method comprises receiving a plurality of food records from a plurality of sources, each of the plurality of food records comprising at least a food record description, the plurality of sources including (i) at least one non-user entity that is an owner of a third party food database and (ii) users of the food record database. The method further comprises receiving search requests from users, and returning one or more top search results from the food record database in response. The method also comprises determining a score for a particular food record identified by the top search results, wherein the score is calculated based at least in part on one of: a number of times the particular food record has been included in the top search results of the search requests or a number of times the particular food record has been logged.