Extended Query Syntax for Multi-Table Data Integration
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Solution Overview
Problem
Conventional data storage and querying technologies face challenges in managing and interoperating large, complex datasets across disparate platforms, leading to inefficiencies and resource-intensive processes due to the need for manual manipulation and integration of multiple tables, which limits data operations and analysis capabilities.
Innovation Solution
A collaborative dataset consolidation system that employs an extended computerized query language syntax to analyze multiple tabular data arrangements, allowing for the transformation of datasets into graph data formats and enabling implicit federated queries, thereby simplifying the process of accessing and querying multiple datasets without the need for extensive manual integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data storage and querying technologies are used to manage large, complex datasets across disparate platforms, then data can be stored and accessed, but manual manipulation and integration of multiple tables are required, leading to inefficiencies and resource-intensive processes
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between disparate data platforms and the querying process. This intermediary automatically integrates multiple tables from different platforms, eliminating the need for manual manipulation while maintaining data interoperability across heterogeneous systems.
Solution Approach 2:
The system implements self-service capabilities where the querying mechanism automatically performs data integration and table joining without requiring manual intervention. The system autonomously handles the complexity of integrating multiple tables across disparate platforms, allowing users to simply execute queries without dealing with integration overhead.
2Loss of time
If multiple tabular data arrangements are queried using conventional methods, then data can be retrieved, but extensive manual integration is needed, increasing time and resource consumption
Solution Approach 1:
The system performs preliminary actions by pre-integrating and pre-processing multiple tabular data arrangements before queries are executed. Data from multiple sources is预先 integrated and organized, so when a query is run, the system can retrieve results immediately without requiring manual integration at query time, significantly reducing time loss.
Solution Approach 2:
The patent merges multiple tabular data arrangements into a unified data structure that can be queried as a single integrated source. This combining of multiple tables into one cohesive structure eliminates the need for manual integration during query execution, making the process both faster and simpler to operate.
3Adaptability or versatility
If datasets are transformed into graph data formats and implicit federated queries are enabled, then data interoperability improves, but extended computerized query language syntax is required
Solution Approach 1:
The extended computerized query language syntax is designed to be universal, capable of handling multiple data formats including both traditional tabular structures and graph data formats. This multi-functional query language can adapt to different data arrangements without requiring separate query mechanisms, thereby improving interoperability while managing complexity through a unified approach.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and wired and wireless network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform configured to provide one or more computerized tools that facilitate development and management of data projects, including implementation of extended computerized query language syntax to analyze, for example, multiple tabular data arrangements in data-driven collaborative projects. For example, a method may include generating data to present a query editor in a data project interface, receiving data representing a first query command to select one or more subsets of data, identifying in the data representing a second query command a subset of datasets from which to extract the data, and applying a query based on a first query command and a second query command.