Project Data Storage With ML-Mapped Nested Tables
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Solution Overview
Problem
Non-standardized data categorizations across industries and entities lead to inefficient storage and lack of meaningful insights due to varied naming conventions, resulting in excessive storage space consumption and inflexible or broad data categories.
Innovation Solution
Utilizing trained machine learning models to determine predicted data category mappings and nesting data tables, allowing for standardized data categorizations that optimize storage capacity while maintaining user flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple separate tables are created for different data categorizations, then data storage capacity increases, but storage space consumption increases significantly
Solution Approach 1:
The patent implements nested tables where related data categories are organized hierarchically with parent-child relationships. Each nested table contains data for a parent category and its subordinate categories, allowing multiple categorizations to be stored within a single consolidated table structure rather than requiring separate tables for each category.
Solution Approach 2:
The patent consolidates multiple separate data tables into a single nested table structure by merging related categories. This combining approach maintains all necessary data storage capacity while reducing the total number of separate tables and files required, thereby decreasing overall storage space consumption.
2Productivity
If standardized data categorizations are implemented, then storage efficiency improves, but flexibility for custom naming conventions is lost
Solution Approach 1:
The patent segments the data categorization system into standardized parent categories and customizable child categories. The parent categories provide the standardized structure for storage efficiency, while child categories allow users to maintain custom naming conventions and specific categorizations relevant to their needs, thus preserving flexibility.
Solution Approach 2:
The patent applies different levels of standardization at different hierarchical levels. Parent categories use standardized naming conventions to improve storage efficiency, while child categories allow local customization to maintain flexibility. This local quality approach enables each level of the hierarchy to serve its specific function optimally.
Data Source
AI summary
Techniques for optimizing project data storage are disclosed. An example system includes processors and memories storing a machine learning (ML) model and instructions that cause the processors to: execute the ML model to determine a predicted data category mapping for a first data category to a normalized data category, execute a set of instructions to: input the first data category into a first table, collapse the first table with a second table by adjusting identification values associated with (i) the first data category or (ii) a second data category in the second table into a path value that references both the first data category and the second data category and is stored in a third table, and store the third table in a project database. The third table has a file size that is less than a combined file size of the first table and the second table.