Hierarchical Data Query Sets for Versioned Database Updates
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Solution Overview
Problem
Existing systems face inefficiencies in querying and managing hierarchically-structured data, leading to increased processing power consumption, memory requirements, and data redundancy, particularly in medical coding systems where versioning and data accuracy are critical.
Innovation Solution
The implementation of hierarchically-structured data and query sets that divide data into manageable segments, provide contextual clues for querying, and utilize revision control to manage data versions, reducing the need for redundant storage and processing by allowing both versions to be stored and updated systematically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hierarchical data is stored in traditional flat database structures, then data retrieval requires full scans and complex processing, but this leads to increased processing power consumption and memory requirements
Solution Approach 1:
The patent segments hierarchical data into versioned components (first version and second version) stored in a database, allowing selective querying of specific versions rather than scanning entire datasets. This segmentation enables efficient retrieval by directly accessing targeted versioned records, reducing processing power consumption and improving retrieval speed.
2Reliability
If all versions of hierarchical data are stored simultaneously, then version control and data accuracy are maintained, but this increases data redundancy and storage requirements
Solution Approach 1:
The patent implements a nested versioning structure where multiple versions of hierarchical data are organized in a hierarchical database framework. Each version is nested within the overall data structure with clear version identifiers, allowing the system to maintain multiple versions for accuracy and reliability while the hierarchical organization minimizes redundancy through shared common elements across versions.
3Productivity
If complex queries are executed on large datasets, then comprehensive data analysis is achieved, but this increases processing time and computational resources
Solution Approach 1:
The patent performs preliminary actions by pre-organizing hierarchical data into versioned segments with structured metadata and hierarchical relationships before queries are executed. This preliminary structuring allows the database to efficiently process complex queries by leveraging the pre-established versioning and hierarchy, reducing processing time while maintaining comprehensive data analysis capability.
4Reliability
If traditional database updating methods are used, then data synchronization is achieved, but this leads to increased errors and manual intervention requirements
Solution Approach 1:
The patent implements feedback mechanisms through automated query execution that retrieves updated hierarchical data from the database and applies changes to datasheets. The system monitors for outmoded hierarchical data, executes queries to retrieve current versions, and automatically updates datasheets, providing continuous feedback loops that ensure data synchronization while reducing manual intervention and errors.
Data Source
AI summary
A method for querying hierarchical data from a first database including receiving first hierarchical data associated with a first version and storing the first hierarchical data in the first database of the provider computing system. The method includes receiving a request to generate a query set including a first query and a second query. The method includes generating the query set. The method includes receiving second hierarchical data associated with a second version and storing the second hierarchical data in the first database of the provider computing system. The method includes executing the query set on the database to select matching hierarchical data, in response to storing the second hierarchical data in the first database. The method includes selecting a datasheet including outmoded hierarchical data. The method includes replacing the outmoded hierarchical data with the matching hierarchical data and storing the datasheet in the second database.