Composite Data Definition for Loosely-Structured Data Parsing
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Solution Overview
Problem
Existing methods for identifying data items in loosely-structured data, such as Regular Expressions, are inefficient and difficult to maintain due to their limitations in defining relationships between multiple data items, leading to complex and unmaintainable regular expressions and high development and maintenance costs.
Innovation Solution
A computer-implemented method for identifying items in loosely-structured data using a composite data definition that includes a layout indicating positional relationships and an identification order list, allowing for efficient parsing and identification of data items through a visual editor and algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If Regular Expressions with lookarounds are used for parsing loosely-structured data, then identification capability is improved, but expression complexity and maintenance difficulty increase significantly
Solution Approach 1:
The patent segments the complex pattern matching task into multiple simpler Regular Expressions, each responsible for identifying a specific data item type (e.g., one for first name, another for last name, another for address). This segmentation allows each expression to remain simple and maintainable while collectively handling complex loosely-structured data parsing through coordinated execution.
2Productivity
If custom executable modules are used for every composite data structure, then parsing efficiency and adaptability are improved, but development cost and maintenance cost increase
Solution Approach 1:
The patent creates a universal parsing framework that uses a single executable module capable of handling multiple composite data structures. The system achieves adaptability through a configurable identification order list and reusable Regular Expressions that can be applied across different data types (names, addresses, URLs), eliminating the need for separate custom modules for each composite structure.
3Adaptability or versatility
If Regular Expressions are used for identifying multiple data items with complex relationships, then identification power is improved, but maintainability and synchronization of identical patterns deteriorate
Solution Approach 1:
The patent segments identical or similar pattern matching logic into separate, reusable Regular Expressions stored in a registry. Each expression is maintained independently and can be referenced multiple times through the identification order list, ensuring that updates to a single expression automatically synchronize across all usage points without manual intervention.
Solution Approach 2:
The patent uses copying by creating references to the same Regular Expression objects in multiple positions within the identification order list. Instead of duplicating expression text, the system copies the reference to the underlying expression object, ensuring that any modification to the original expression is automatically reflected wherever it is referenced, maintaining consistency without duplication overhead.
Data Source
AI summary
A computer-implemented method is provided for identifying items in loosely-structured data. The method generally includes constructing a composite data definition for items which are to be identified and parsing input data using the data definition. The composite data definition is constructed by defining a layout for a composite data definition, where the layout indicates at least one of positional relationship of data items to each other and positional information for data items in the loosely-structured data; arranging data items in the layout, where each data item in the layout has a common meaning for applications that use the data item; creating an identification order list for the composite data definition, where the identification order list includes the data items in the layout and specifies an order in which the data items comprising the composite data definition are to be identified within the loosely-structured data.