Bidirectional Indexing for Interactive Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data management and analysis systems face challenges in efficiently handling large datasets with numerous tables and fields, as they struggle to provide interactive and efficient data retrieval and analysis, especially when dealing with complex user selections and associations across multiple data points.
Innovation Solution
The method involves generating bidirectional table indexes (BTI) and bidirectional association indexes (BAI) based on a data model, which allows for in-memory loading and efficient data retrieval by determining binary states of fields and tables, enabling dynamic user interface updates and calculations of hypercubes based on user selections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data management systems are used to handle large datasets with numerous tables and fields, then data retrieval and analysis can be performed, but the systems struggle to provide interactive and efficient data retrieval especially when dealing with complex user selections and associations across multiple data points
Solution Approach 1:
The patent segments the data model into multiple independent bidirectional indexes (BTI and BAI), each handling specific aspects of data relationships. This segmentation allows the system to process and retrieve data in smaller, manageable units rather than handling the entire complex dataset at once, thereby improving retrieval efficiency while maintaining manageable system complexity.
Solution Approach 2:
The patent introduces bidirectional indexes that create additional dimensional layers for data access. By organizing data relationships in multiple dimensions through BTI (table-level) and BAI (association-level) indexes, the system enables efficient navigation across complex data associations without increasing operational complexity for users.
2Productivity
If bidirectional table indexes and bidirectional association indexes are generated and loaded in-memory, then memory requirements are reduced and data analysis efficiency is enhanced, but the initial processing and index generation require additional computational resources
Solution Approach 1:
The patent applies preliminary action by pre-generating and loading bidirectional indexes into memory before actual data analysis operations. Although index generation requires computational resources, this upfront investment creates optimized data structures that enable significantly faster data retrieval and analysis during subsequent operations, reducing the need for repeated heavy processing.
Solution Approach 2:
The patent creates simplified copies of data relationships in the form of bidirectional indexes stored in memory. These indexes are condensed representations of the actual data that can be quickly accessed and manipulated, reducing the computational power needed for analysis operations while maintaining accurate representation of data associations.
3Ease of operation
If the system determines distinct values in all related tables and calculates hypercubes based on user selections, then interactive data analysis is provided, but the calculation and processing time increases for complex selections
Solution Approach 1:
The patent pre-calculates and stores bidirectional indexes that capture relationships between tables and associations before user interactions occur. When users make selections, the system can quickly retrieve pre-established index structures rather than performing complex distinct value calculations and hypercube computations from scratch, dramatically reducing response time for interactive analysis.
Solution Approach 2:
The bidirectional indexes serve as intermediary structures between the raw data and the user interface. These indexes mediate the complex calculations by providing pre-organized data relationships that can be quickly queried and combined, enabling interactive analysis without requiring real-time computation of all possible associations and distinct values.
Data Source
AI summary
In an aspect, provided is a method comprising receiving a data model, generating a bidirectional table index (BTI) based on the data model, generating a bidirectional association index (BAI) based on the data model and the bidirectional table index, and loading a portion of the data model, the BAI, and the BTI in-memory.