Multi-Level Hyper-Table for Large Data Set Access
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Solution Overview
Problem
Analyzing and interpreting large data sets is difficult due to their cumbersome size, making it time-consuming and error-prone for users to identify high frequency correlations within these sets.
Innovation Solution
The implementation of multi-level tables, where hyper-cells group information from initial base tables, allowing for concise and accessible representation of data through multiple levels, enabling users to access and identify correlations without reviewing the entire table.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a system returns a complete data set of a thousand or a million entries, then the data completeness is improved, but the interpretability and user accessibility deteriorates
Solution Approach 1:
The patent divides a large data set into multiple hierarchical levels (e.g., summary level, detail level). The summary level presents aggregated data to maintain completeness while the detail level provides access to individual entries, resolving the contradiction between data completeness and user interpretability.
Solution Approach 2:
The patent adds a hierarchical dimension to data presentation, transforming a flat large-scale table into a multi-level structure. This allows users to navigate from high-level summaries down to specific entries, maintaining data completeness while improving interpretability through progressive disclosure.
2Ease of operation
If the number of rows or columns is limited to a smaller value, then the table size is reduced for better accessibility, but the data completeness and correlation identification capability deteriorates
Solution Approach 1:
The patent segments the data table into hierarchical levels where summary tables contain aggregated information from underlying detailed tables. This allows presentation of smaller, more accessible tables while preserving access to the complete data set through drill-down capabilities.
Solution Approach 2:
The patent implements nested table structures where summary tables are embedded within or linked to detailed tables. Each level contains references to the next level, allowing users to access complete data through nested navigation while maintaining small visual footprints at each level.
3Reliability
If a user reviews the entire table to identify high frequency correlations, then the analysis completeness is improved, but the time consumption and error rate increases
Solution Approach 1:
The patent performs preliminary aggregation and summarization of data at higher levels before presentation to users. Summary statistics, aggregated metrics, and pre-computed correlations are displayed first, allowing users to quickly identify patterns without reviewing entire data sets, thus reducing analysis time while maintaining reliability.
Data Source
AI summary
Methods, systems and computer readable media are provided for accessing data utilizing a multi-level table comprising generating a plurality of levels of the multi-level table, wherein a first level of the multi-level table includes a hyper-table with a plurality of hyper-cells each hyper-cell including information for a group of cells from an initial base table, wherein intermediate levels of the multi-level table each include a plurality of hyper-tables comprising hyper-cells with each hyper-table linked to and providing information for a corresponding hyper-cell of a hyper-table of a prior level, and wherein a plurality of tables of a terminal level includes information from cells of the initial base table with each table linked to and providing information for a corresponding hyper-cell. Data from the multi-level table is accessed by traversing links between the hyper-tables of the plurality of levels to access data within the tables of the terminal level.