Database Query Data Structures for Computational Efficiency
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Solution Overview
Problem
Current query-based architectures for data analysis in large databases are inefficient as they extract a subset of data, breaking associations and losing valuable information, leading to computational and time-intensive processes while failing to present comprehensive knowledge.
Innovation Solution
The method involves reading database records, instantiating initial and final data structures to reflect unique data element types and values, and generating query structures to identify relevant records, allowing for non-redundant data selection and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If query-based architectures extract a subset of data from large databases, then computational time and processing complexity are reduced, but associations between data elements are broken and valuable information is lost
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing data summaries, aggregates, and relationships in a materialized form before queries are executed. This allows the system to quickly retrieve pre-prepared information without performing complex computations during query execution, thus reducing computational time while preserving data associations and knowledge that would otherwise be lost in traditional query extraction.
Solution Approach 2:
The patent creates simplified copies or representations of the large database structure, such as summary tables, aggregate data structures, or pre-computed relationship graphs. These copies retain essential associations and knowledge from the original database while being much smaller and faster to query, effectively resolving the contradiction between speed and information preservation.
2Ease of operation
If query-based architectures extract a subset of data from large databases, then the result set is simplified for user focus, but the process becomes computationally intensive and time-consuming
Solution Approach 1:
The system performs preliminary organization and filtering of data into structured summaries and aggregates before users need to access them. This pre-processing maintains data associations and preserves knowledge while creating a simplified, user-friendly view that does not require intensive computation during actual query execution, thereby improving both ease of operation and productivity.
3Device complexity
If traditional query methods completely divorce the result set from the database, then computation is simplified, but associations and contextual knowledge are lost
Solution Approach 1:
The patent introduces intermediary data structures such as summary tables, aggregate structures, and relationship graphs that serve as mediators between the full database and the final result set. These intermediaries preserve data associations and contextual knowledge while presenting simplified, computation-efficient views to users, effectively acting as a bridge that maintains both simplicity and information integrity.
Data Source
AI summary
Provided are methods comprising receiving a query for information from a database, determining particular data element types and data element values that are the subject of the query, instantiating a query data structure containing the data element types and the data element values that are the subject of the query, identifying records within the database that contain one or more data element types and/or data element values that are included in the query data structure, and instantiating a results data structure comprising information relating to the identified records.