Automated Tree-Structured Document to Table Conversion
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Solution Overview
Problem
Extracting data from tree-structured documents and converting it into tabular format is labor-intensive and requires programming expertise, especially when the documents have complex structures or require sophisticated processing.
Innovation Solution
A data cleaning tool that automatically extracts and converts data from tree-structured documents into tables by analyzing the document structure, constructing a schema, selecting a conversion scheme based on user behavior or explicit input, and applying it to generate the output table, or by synthesizing programs to meet specific extraction and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a customized script is written to extract data from tree-structured documents, then data extraction and table construction can be performed, but the process becomes labor-intensive and requires programming expertise
Solution Approach 1:
The system performs self-service by automatically analyzing the tree-structured document format, inferring the desired table structure, and executing the extraction process without requiring a data cleaner to write custom scripts. The automated script generation and execution eliminates manual programming while maintaining extraction capability
Solution Approach 2:
The system performs preliminary action by pre-defining a library of extraction schemes and table construction templates that can be automatically applied to tree-structured documents. This preparation of extraction patterns in advance eliminates the need for ad-hoc script writing for each extraction task
2Productivity
If conventional approaches are used for data extraction, then programming expertise is required, but this increases the time and effort needed for data cleaning
Solution Approach 1:
The system eliminates the need for data cleaners to write scripts by having the system automatically generate and execute extraction code based on the tree document structure and desired output format, thereby recovering the time previously spent on script writing
Solution Approach 2:
The system introduces an intermediary automated script generation layer between the tree-structured document and the final table output. This intermediary automatically translates the document structure into extraction logic, eliminating the need for manual script writing while maintaining extraction flexibility
3Ease of operation
If automated script generation is implemented, then programming expertise is not needed, but the system complexity increases
Solution Approach 1:
The system performs self-service by automatically analyzing tree document structures, inferring extraction logic, and generating executable scripts without user intervention. This automation handles the complexity internally while presenting a simple interface to users
Solution Approach 2:
The system segments the complex automated extraction process into distinct modular components: document structure analysis, extraction scheme selection, script generation, and execution. This segmentation manages system complexity through modularity while maintaining ease of operation
Data Source
AI summary
Various technologies pertaining to extracting data encoded in a tree-structured document and generating a table based upon the extracted data are described herein. In a first embodiment, the table is generated without requiring input from a data cleaner. In a second embodiment, the table is generated based upon examples set forth by a data cleaner.