Unstructured Data Classification via Relational Similarity Index
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Solution Overview
Problem
Current methods for managing unstructured data face challenges due to its irregular nature, rapid growth, and complexity, making it difficult to classify and manage effectively, especially in large datasets where existing approaches require significant resources or reduce accuracy.
Innovation Solution
The proposed solution involves a classification engine that uses term processing, noise filtering, and inverse document frequency to generate relational similarity indexes, allowing for efficient grouping and management of unstructured data items by determining their similarity and relevance, adapting to dynamic conditions without the need for extensive training or resource-intensive processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification methods are used on large unstructured data sets, then classification accuracy can be maintained, but resource consumption and processing time increase significantly
Solution Approach 1:
The patent extracts only the most relevant terms from unstructured data items using term frequency and inverse document frequency calculations, filtering out noise and less significant terms. This extraction approach reduces the amount of data that needs to be processed while maintaining classification accuracy, directly addressing the resource consumption issue.
Solution Approach 2:
The classification process is segmented into distinct stages: term extraction, term ranking using IDF, similarity calculation, and classification decision. This segmentation allows the system to process data in manageable chunks rather than handling entire large datasets at once, reducing resource consumption while maintaining accuracy.
2Reliability
If comprehensive term processing is performed on all data items, then classification reliability improves, but processing time increases
Solution Approach 1:
The system performs preliminary term extraction and IDF calculation on a corpus of data items before actual classification is needed. By pre-processing the data to identify and rank important terms in advance, the system reduces the time required during actual classification operations while maintaining reliable results.
Solution Approach 2:
Different levels of processing are applied to different terms based on their importance. High-frequency and high-IFD terms receive full processing attention, while low-importance terms are processed more lightly or skipped entirely. This local quality approach maintains classification reliability for critical terms while reducing overall processing time.
3Adaptability or versatility
If the classification system is designed to handle dynamic and changing data, then adaptability improves, but system complexity increases
Solution Approach 1:
The system dynamically adapts to changing data by continuously updating its term frequency and inverse document frequency indices as new data items are added to the corpus. This dynamic updating mechanism allows the classification system to adapt to evolving data patterns without requiring complex retraining procedures, maintaining simplicity while improving adaptability.
Data Source
AI summary
Methods, computer program products, and computer systems for the classification of unstructured data items are disclosed. Such methods, computer program products, and computer systems include ingesting an item into a classification engine, performing term processing on one or more terms of the item, and processing a relational similarity index. The classification engine is implemented in the computer system. The relational similarity index represents a similarity of the item to a reference item, and the relational similarity index is determined using the one or more terms.