Link-Formative Queries for Collaborative Dataset Interoperability
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Solution Overview
Problem
Conventional data storage and computing technologies face challenges in facilitating data interoperability among disparate datasets due to incompatible systems, manual intervention requirements for data standardization, and inefficient index-based queries, leading to suboptimal performance and friction in data operations.
Innovation Solution
A collaborative dataset consolidation system that transmutes relationships among datasets, converting data between tabular and graph formats to enable interoperability, automate data standardization, and optimize queries by forming transmuted associations, allowing for seamless interaction and analysis across different data formats and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual intervention is used to standardize data arrangements, then data consistency is improved, but labor intensity and time consumption increase
Solution Approach 1:
The system enables automated data standardization where the data processing system itself performs the standardization of data arrangements without requiring manual intervention. The system automatically identifies, analyzes, and standardizes data from disparate formats and structures, making the system self-sufficient in maintaining data consistency.
Solution Approach 2:
Manual mechanical processes of data standardization are replaced with automated computational processes. The system uses algorithms and automated analysis to identify data patterns, determine appropriate data arrangements, and standardize data structures, replacing the need for manual data practitioner intervention.
2Ease of operation
If index-based queries are used to join data in different tables, then data retrieval is enabled, but computational performance deteriorates
Solution Approach 1:
The system performs preliminary analysis of data characteristics and relationships before executing queries. By pre-analyzing data arrangements, identifying optimal join strategies, and preparing data access pathways in advance, the system avoids the computational overhead of traditional index-based queries during actual data retrieval operations.
Solution Approach 2:
The system changes the fundamental parameters of data access by moving away from traditional index-based query mechanisms to alternative data access strategies. This includes changing how data is organized and accessed, fundamentally altering the query execution model to improve computational performance while maintaining ease of data retrieval.
3Quantity of substance
If conventional data storage systems are used to store disparate datasets, then data storage is achieved, but data interoperability deteriorates
Solution Approach 1:
The system creates a universal data processing framework that can handle multiple data formats, structures, and arrangements simultaneously. The automated analysis and standardization capabilities enable the system to work with diverse datasets from different sources and formats, providing multi-functional data processing that enhances interoperability while maintaining storage capacity.
Solution Approach 2:
The system introduces automated analysis and standardization as an intermediary layer between disparate datasets and the data processing operations. This intermediary automatically translates and standardizes data from various formats and structures into a unified representation, enabling interoperability without requiring changes to the underlying storage systems.
4Ease of operation
If data practitioners manually intervene to group data, then data organization is improved, but friction and complexity increase
Solution Approach 1:
The system performs automated data grouping and organization without requiring manual intervention from data practitioners. The system autonomously analyzes data characteristics, identifies appropriate grouping criteria, and organizes data accordingly, making the system self-sufficient in data organization tasks.
Solution Approach 2:
Manual data grouping processes are replaced with automated computational algorithms. The system uses automated analysis to determine optimal data groupings based on data attributes, relationships, and organizational requirements, eliminating the need for manual data practitioner intervention and reducing system complexity.
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 link-formative queries to form, for example, interrelations among a system of networked collaborative datasets. For example, a method may include analyzing a dataset to detect data values with which to query against in a link-formative query, applying a link-formative query to a dataset, identifying results of the link-formative query, and forming an enhanced dataset to include results a link-formative queries in the dataset.