JSON Query Parser Using Context-Free Grammar for Cloud Databases
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Solution Overview
Problem
Existing technologies face challenges in efficiently parsing complex logical queries for cloud resources using JSON queries, particularly in handling nested structures and obfuscated data within software-as-a-service cloud databases.
Innovation Solution
A cloud database query parser is developed using a context-free grammar with first-order logic, generating a parse tree to identify JSON object paths and retrieve corresponding cloud resources, enabling the handling of complex logical queries by communicating with an information retrieval model database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a cloud database query parser is developed using context-free grammar with first-order logic to handle complex logical queries, then the ability to manage complex queries and identify cloud resources from obfuscated data is enhanced, but the device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The parser is divided into distinct functional modules: a grammar analysis module that processes context-free grammar rules, a first-order logic evaluation module that handles logical queries, and a cloud resource identification module that extracts resources from obfuscated data. Each module operates independently with defined interfaces, reducing overall system complexity while maintaining the ability to handle complex queries.
Solution Approach 2:
An information retrieval model database serves as an intermediary layer between the query parser and the actual cloud database. This database stores pre-processed cloud resource information and metadata, allowing the parser to query resources without directly complex interactions with the underlying database system, thus simplifying the parser's complexity while enhancing its adaptability.
2Adaptability or versatility
If a cloud database query parser is developed using context-free grammar with first-order logic to handle complex logical queries, then the ability to manage complex queries and identify cloud resources from obfuscated data is enhanced, but the difficulty of detecting and measuring increases
Solution Approach 1:
The manual and complex process of parsing first-order logic queries is replaced by an automated parser system based on context-free grammar. The parser automatically tokenizes, parses, and evaluates logical queries using predefined grammar rules, eliminating the need for manual query construction and reducing the difficulty of detecting and measuring query complexity.
Solution Approach 2:
The system creates and maintains copies of cloud resource information in the information retrieval model database, including metadata and structural information. These copies allow the parser to work with simplified representations of complex cloud resources, making detection and measurement easier while maintaining the ability to handle complex queries.
Data Source
AI summary
A JavaScript Object Notation (“JSON”) query parser parses cloud database JSON queries with first order logic. A compiler-compiler generates the JSON query parser using a representation of a context-free grammar with first-order logic. An information retrieval model generator generates information retrieval models for cloud resources in the cloud database based on known JSON queries and query responses to the cloud database. The JSON query parser generates parse trees from the JSON queries and identifies cloud resources for the JSON queries. Subsequently, the JSON query parser uses the parse trees to extract JSON object paths from the corresponding information retrieval models and the JSON object paths go into a cloud database query.