Automated Data Parsing for Unknown Formats
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data analysis systems are limited in their ability to parse data sets of unknown structure, format, or type, as they often require pre-defined formats and cannot automatically determine appropriate parsing techniques, leading to incorrect parsing or failure when encountering unfamiliar data formats.
Innovation Solution
An automated data parsing system using a Bayesian-related probabilistic approach that determines suitable framing, typing, and tokenization techniques for unknown data sets by evaluating candidate techniques and updating confidence indicators based on evidence, allowing it to parse data sets without prior knowledge of their structure or format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis systems use pre-defined parsing rules for specific data formats, then parsing accuracy for known formats is improved, but the system cannot handle data sets with unknown structures or formats
Solution Approach 1:
The system dynamically adjusts parsing rules based on the input data characteristics. Instead of using static pre-defined rules for specific formats, the system analyzes the data structure and automatically adapts the parsing approach to match the unknown format, enabling both accuracy and versatility
Solution Approach 2:
The system changes parsing parameters and rules based on the detected data characteristics. By monitoring data patterns and adjusting parsing parameters dynamically, the system achieves accurate parsing for unknown formats without requiring pre-defined rules for each specific format
2Reliability
If the system uses data format-specific parsing rules, then parsing reliability for supported formats is improved, but the system fails when encountering unfamiliar data formats
Solution Approach 1:
The system performs preliminary analysis of the data structure before applying parsing rules. By examining data patterns, delimiters, and structure in advance, the system prepares appropriate parsing rules for unknown formats, ensuring reliable parsing without requiring pre-defined rules for every format
Solution Approach 2:
The system automatically determines and applies appropriate parsing rules for unknown formats without external intervention. The parsing engine self-adapts by analyzing data characteristics and selecting or generating suitable rules, maintaining reliability while handling diverse formats
3Stability of the object's composition
If the system requires pre-defined parsing rules for each data format, then parsing consistency is improved, but the system complexity increases to support multiple formats
Solution Approach 1:
The system uses a universal parsing engine that can handle multiple data formats through a single unified approach. Instead of maintaining separate parsing logic for each format, the system employs general-purpose rule generation and adaptation mechanisms that work across diverse formats, reducing complexity while maintaining consistency
Solution Approach 2:
The system introduces an intermediate rule generation layer between the data input and parsing execution. This intermediary component automatically generates or selects appropriate parsing rules based on data characteristics, providing consistent parsing behavior without requiring explicit pre-defined rules for each format in the main system
4Productivity
If the system assumes a specific data format for unknown inputs, then parsing speed is improved, but parsing accuracy deteriorates due to incorrect format assumptions
Solution Approach 1:
The system performs partial format detection and assumption only for the portion of data that can be quickly analyzed. By applying lightweight format detection to identify obvious patterns and assumptions for the remaining data, the system achieves both speed and accuracy without requiring complete format verification before parsing
Data Source
AI summary
A framing technique included in a set of framing techniques is applied to at least a portion of a target data set. When a result of the application of the framing technique meets a first condition, a typing technique included in a set of typing techniques is applied to the target data set. When a result of the application of the typing technique meets a second condition, a tokenization technique included in a set of tokenization techniques is applied to the target data set. When a result of the application of the tokenization technique meets a third condition, a parsing technique for the target data set is determined to include the framing technique, the typing technique and the tokenization technique. An indication of the parsing technique is generated.