Automated Structuring of Free Form Heterogeneous Data
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Solution Overview
Problem
Existing approaches to structuring free form heterogeneous data in technical helpdesk systems are inefficient due to the unstructured and noisy nature of ticketing data, leading to difficulties in searching for relevant problem resolutions.
Innovation Solution
The method involves segmenting and labeling free form data using machine learning techniques to structure it in a format that facilitates IT operations, employing supervised and semi-supervised learning algorithms to identify key information structures and generate annotation models for automatic labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If free form heterogeneous data is stored in unstructured format, then storage flexibility is maintained, but search efficiency and information retrieval accuracy deteriorate
Solution Approach 1:
The patent segments free form heterogeneous data into distinct structured fields including problem description, resolution steps, product information, and environment details. This segmentation transforms unstructured text into organized data elements that can be efficiently queried and retrieved, directly improving search efficiency while maintaining manageable complexity through systematic categorization.
2Measurement precision
If manual labeling and structuring of ticket data is performed, then data quality and search accuracy improve, but processing time and labor costs increase
Solution Approach 1:
The patent implements automatic labeling and structuring systems that enable the helpdesk data to self-organize into structured formats without extensive manual intervention. Machine learning algorithms and automated text processing techniques allow the system to independently categorize and structure data, maintaining high information retrieval accuracy while dramatically reducing the time and labor required compared to manual processing.
3Ease of manufacture
If existing word-based heuristic rules are used for data classification, then implementation simplicity is maintained, but classification accuracy and relevance deteriorate due to noisy heterogeneous data
Solution Approach 1:
The patent transitions from simple word-based heuristic rules to more sophisticated classification parameters including n-gram analysis, contextual understanding, and multiple feature extraction. This parameter enhancement allows the system to accurately classify noisy heterogeneous data by considering broader contextual patterns rather than relying solely on individual keyword matches, thereby improving classification accuracy while maintaining reasonable implementation complexity.
Data Source
AI summary
Techniques are provided for automatically structuring free form heterogeneous data. In one aspect of the invention, the techniques include obtaining free form heterogeneous data, segmenting the free form heterogeneous data into one or more units, automatically labeling the one or more units based on one or more machine learning techniques, wherein each unit is associated with a label indicating an information type, and structuring the one or more labeled units in a format to facilitate one or more operations that use at least a portion of the labeled units, e.g., information technology (IT) operations.