DSV Data Parsing via Layered Character Categorization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analytic systems face challenges in reliably processing data of different formats without errors, as they struggle to distinguish between binary and text data, leading to potential errors in data processing.
Innovation Solution
A schema determination system is introduced, comprising a content type determination engine, delimiters determination engine, DSV data parser, and DSV field names determination engine, which determines the content type, delimiters, and field names in input data, allowing the system to accurately process delimiter-separated value (DSV) data and other formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes data without format detection, then processing speed is maintained, but data processing reliability deteriorates due to inability to distinguish between binary and text data
Solution Approach 1:
The system performs preliminary content type detection before processing data to determine whether the data is binary or text format. This preliminary action enables the system to select the appropriate processing path in advance, preventing processing errors while maintaining efficient data handling through format-aware processing
2Measurement precision
If the system uses a simple data processing approach, then device complexity is reduced, but measurement precision deteriorates in determining data format and delimiters
Solution Approach 1:
The detection system is segmented into multiple specialized engines: content type determination engine, delimiters determination engine, and DSV data parser. Each engine handles a specific aspect of data format detection independently, improving overall measurement precision while allowing modular complexity management
Solution Approach 2:
The system introduces an intermediary detection layer between data input and processing that includes content type determination and delimiter identification components. This intermediary layer accurately characterizes data formats before processing occurs, enabling precise format-aware processing without complicating the core processing logic
3Reliability
If the system does not resolve field name conflicts, then processing speed is maintained, but data processing reliability deteriorates due to ambiguous field identification
Solution Approach 1:
The DSV field names determination engine automatically detects and resolves field name conflicts without requiring manual intervention or external reference to data dictionaries. The system self-services by analyzing field name patterns, identifying duplicates, and generating resolved field names autonomously, improving reliability while minimizing time overhead
Data Source
AI summary
In some examples, a system receives delimiter separated value (DSV) data, and categorizes a character in the DSV data into a selected layer of a plurality of layers, where characters in a first layer of the plurality of layers comprise data characters, characters in a second layer of the plurality of layers comprise delimiters, and characters in a third layer of the plurality of layers comprise grouping symbols to group a string of characters into a semantic unit. The system parses the DSV data according to the categorizing.