Transmuting Data Associations for Collaborative Dataset Interoperability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data storage and computing technologies face challenges in facilitating data interoperability among disparate datasets due to incompatible formats, manual intervention requirements, and inefficient index-based associations, leading to suboptimal performance and friction in data operations.
Innovation Solution
A collaborative dataset consolidation system that transmutes relationships between datasets, converting data from one format to another, such as from tabular to graph, to enable queries across different data structures and formats, using a dataset ingestion controller and data analyzer to form transmuted associations and enhance querying capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data storage technologies are used to store disparate datasets in different formats, then data can be preserved in its original structure, but data interoperability is blocked and manual intervention is required
Solution Approach 1:
The patent introduces an intermediary layer (transmuted data structure with graph model and identifiers) that mediates between disparate data formats. This intermediary enables automatic association and interoperability without requiring manual intervention to bridge different data structures, as the intermediary provides a universal interface for data integration.
Solution Approach 2:
The patent transforms data from conventional formats into a transmuted format with specific parameters (identifiers, graph relationships, data arrangement types). This parameter transformation enables automated processing and interoperability by converting diverse data into a standardized representation that can be automatically associated through identifier matching.
2Reliability
If index-based associations are used to join data in different tables, then data relationships can be established, but computational performance is impeded due to increased computation requirements
Solution Approach 1:
The patent creates a copy of the association relationship in the form of identifier-based references within the transmuted data structure. Instead of performing computational comparisons during queries, the system pre-establishes identifier links that directly represent relationships, eliminating the need for repeated index computations while maintaining accurate data relationships.
Solution Approach 2:
The patent performs preliminary action by establishing identifier-based associations during data ingestion and transformation, rather than during query execution. This pre-computation of relationships stored in the transmuted data structure eliminates the need for costly index-based computations during subsequent data operations, significantly improving computational performance.
3Stability of the object's composition
If manual standardization of data arrangements is performed, then data consistency can be achieved, but sufficient friction is caused to dissuade data usage
Solution Approach 1:
The patent implements self-service by enabling the system to automatically standardize and associate data through identifier matching and graph model construction during automated processes. This eliminates the need for manual standardization intervention while maintaining data consistency, as the system autonomously performs the standardization function that would otherwise require human operators.
4Reliability
If conventional data formats and structures are maintained, then data integrity is preserved, but data interoperability among different formats is not enabled
Solution Approach 1:
The patent segments data into discrete units with unique identifiers and represents relationships as separate graph edges. This segmentation allows different data formats to be broken down into standardized components that can be reassembled and interconnected through the graph model, enabling interoperability while preserving the integrity of individual data units through their identifier-based identity.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis and computer software and systems to provide an interface between repositories of disparate datasets and computing machine-based entities that seek access to the datasets, and, more specifically, to a computing and data storage platform configured to transmute associations between data arrangements of different formats or different data models to facilitate data operations, such as queries, configured to enhance, for example, an ingested dataset via transmuted associations as, for example, interrelations among a system of networked collaborative datasets. For example, a method may include identifying a referential indicator, determining an association with a value representative of the referential indicator to an equivalent value representative of another referential indicator associated with a different dataset, transmuting the association to form a transmuted association as a link between the value and the equivalent value, and integrating the link into an ingested data arrangement.