Semi-Structured Data Processing System with Dynamic Schema Generation
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Solution Overview
Problem
Processing semi-structured data is challenging due to the lack of a unified structure or schema, as different documents may have varying fields, overlapping fields stored in different ways, or collections comprising arrays of fields or subdocuments, making it difficult to access and manipulate efficiently.
Innovation Solution
Generating structural information by analyzing semi-structured documents to create a visualization and output data set, using a computer system with a semi-structured data processor that includes a database accessor, sampler, analyzer, and visualizer to determine field frequencies, data types, and statistics, allowing users to extract and manipulate data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If semi-structured data is stored without a unified schema to maintain flexibility and adaptability, then data versatility is improved, but data accessibility and processing efficiency deteriorate
Solution Approach 1:
The patent introduces an intermediary schema generation system that analyzes semi-structured data and creates a unified schema representation. This intermediary schema acts as a mediator between the flexible semi-structured data format and the structured querying requirements, enabling efficient data access without imposing rigid constraints on the underlying data storage.
Solution Approach 2:
The system performs preliminary analysis of semi-structured data to generate schemas before querying operations. By pre-processing the data to extract structural patterns and create schemas in advance, the system prepares the data for efficient querying and processing without requiring real-time schema inference during data access operations.
2Ease of operation
If traditional structured data storage with schemas is used to enable efficient querying and processing, then data accessibility is improved, but adaptability to varying data structures deteriorates
Solution Approach 1:
The patent implements dynamic schema generation that adapts to varying data structures. The system analyzes the actual semi-structured data and generates schemas dynamically based on the observed patterns, allowing the schema to evolve and adapt as data structures change, rather than requiring fixed pre-defined schemas.
Solution Approach 2:
The system changes the parameter of schema rigidity by generating flexible schemas that accommodate varying data structures. The generated schemas maintain structural organization for efficient querying while incorporating flexibility to handle different field types, nested structures, and varying data formats through parameterized schema definitions.
3Loss of information
If manual inspection of data items is performed to discover available fields in semi-structured data, then complete data awareness is improved, but time consumption and processing efficiency deteriorate
Solution Approach 1:
The system implements self-service schema generation where the data itself provides the structural information needed for querying. By automatically analyzing the semi-structured data and extracting schemas without requiring manual inspection, the system enables complete data awareness while eliminating the time-consuming manual exploration process.
Solution Approach 2:
The patent replaces the mechanical process of manual data inspection with an automated computational analysis system. The system uses computer-implemented methods to automatically discover data structures, extract schemas, and generate queryable representations, substituting manual human effort with efficient automated processing.
Data Source
AI summary
The present disclosure includes techniques pertaining to computer implemented systems and methods for processing semi-structured data. In one embodiment, semi-structured data in a data source is analyzed and structural information about the data is generated. The semi-structured documents may comprise a plurality of fields having associated values. Documents may be analyzed to determine particular fields in the plurality of semi-structured documents. Statistics may be generated to determine, for example, a frequency that each particular field appears in the plurality of semi-structured documents. Particular fields and associated frequencies and other statistics may be stored in a memory, for example.