Federated Query Platform for Unified Public and Private Data Access
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Solution Overview
Problem
Conventional techniques are inadequate for managing large-scale data access, particularly in integrating public and privately-accessible datasets, as they require expert knowledge of programming languages, databases, and data science topics, and lack the ability to handle disparate data resources effectively.
Innovation Solution
A platform utilizing federated query generation and schema rewriting optimization to provide integrated access to public and privately-accessible datasets, enabling users to query and retrieve data from various sources without needing extensive technical expertise, by converting queries into a unified format and optimizing them for execution across different data storage facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional techniques are used for data access, then data retrieval is possible, but expert knowledge of programming languages and databases is required
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into database-specific query languages. This mediator handles the complexity of database interactions, authentication, and data retrieval, while users only need to provide simple natural language questions, eliminating the need for programming expertise.
Solution Approach 2:
The system implements self-service capabilities where the database interface automatically generates, optimizes, and executes queries based on natural language input. The system autonomously manages authentication credentials, query translation, and result formatting, allowing users to access data without manual configuration or technical knowledge of database operations.
2Adaptability or versatility
If conventional data access methods are used, then data can be retrieved, but integrated access to public and private datasets is not achieved
Solution Approach 1:
The patent creates a universal natural language interface that can query multiple types of data sources (public datasets, private databases, cloud storage) through a single unified system. The same natural language processing engine handles all query types, and the system automatically manages different authentication methods for various data sources, providing versatile access without requiring separate configurations for each data type.
Solution Approach 2:
The system segments different data sources (public and private datasets) into separate accessible components while maintaining a unified query interface. Each data source can be independently configured and accessed with appropriate authentication, while the natural language processing layer provides seamless integration, allowing users to query across segmented data sources without managing their individual complexities.
3Measurement precision
If expert-level query execution is implemented, then precise data retrieval is possible, but usability for general users is limited
Solution Approach 1:
The natural language processing system acts as an intermediary that preserves query precision while improving accessibility. It translates imprecise natural language questions into precise database queries, maintaining the accuracy needed for data retrieval while allowing users to speak in casual, imprecise terms. The system handles the precision requirements internally without exposing them to users.
Data Source
AI summary
Various techniques are described for platform management of integrated access of public and privately-accessible datasets utilizing federated query generation and query schema rewriting optimization, including receiving at a dataset access platform a query formatted according to a first data schema, generating a copy of the query, saving the query and the copy to a datastore, parsing the copy of the query in the first schema using an inference engine, determining whether the query comprises data associated with an access control condition associated with accessing the dataset, the access control condition being configured to indicate whether the query is permitted to access the dataset, and rewriting, using a proxy server, the copy of the query in a second schema, and optimizing the rewriting by identifying a database engine to execute the query and including other data converted into another triple associated with an attribute of the query.