Dynamic Data Structures for Flexible Database Analysis
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Solution Overview
Problem
Current database snapshot technologies are static and inflexible, unable to be easily altered or re-configured, and require specific configuration requirements before creation, limiting their dynamic presentation and analysis capabilities.
Innovation Solution
The methods and systems involve creating initial and final data structures within a database to facilitate dynamic data analysis by instantiating data structures for unique data elements, generating query structures, and providing search results based on these structures, allowing for the capture and modification of dynamic presentations and visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If database snapshots are created to capture data at a specific point in time, then data representation accuracy is improved, but flexibility and adaptability for modification are worsened
Solution Approach 1:
The patent transforms static snapshots into dynamic data structures that can be modified and reconfigured. The system allows users to dynamically adjust query parameters, filter criteria, and visualization settings while maintaining the core data representation, thus achieving both accuracy and flexibility.
Solution Approach 2:
The patent segments the data structure into modular components including data elements, relationships, and visualizations. This segmentation enables independent modification of specific components without affecting the entire snapshot, providing flexibility while preserving overall data representation accuracy.
2Reliability
If database snapshots are created with specific configuration requirements, then data integrity is improved, but ease of operation and configuration is worsened
Solution Approach 1:
The patent implements self-service functionality where the system automatically generates data structures and configurations based on user-defined queries. The system handles complex configuration tasks autonomously, maintaining data integrity while simplifying the user interaction and configuration process.
Solution Approach 2:
The patent allows users to modify parameters such as time ranges, data filters, and visualization settings without requiring reconfiguration of the underlying data structure. This parameter-based approach maintains data integrity while significantly improving ease of operation.
3Measurement precision
If data analysis processes are performed on large databases, then information accuracy is improved, but computational time and processing resources are worsened
Solution Approach 1:
The patent extracts relevant data elements and relationships from the large database to create focused data structures for analysis. By extracting only the necessary information based on user queries and predefined templates, the system maintains information accuracy while significantly reducing computational time and processing resources required.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and indexing data structures before actual analysis queries are executed. This includes pre-establishing relationships, filtering, and organizing data according to common analysis patterns, which accelerates subsequent queries while maintaining accuracy.
Data Source
AI summary
Provided are methods comprising receiving a query for information from the 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.