Unstructured Data Pattern Detection via Signature Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing unstructured data from big data sources are inefficient in identifying common patterns due to the complexity and lack of correlation between data elements, leading to inefficient search and analysis processes.
Innovation Solution
A method and system for extracting unstructured data elements, generating robust signatures, clustering these signatures to identify common patterns, and correlating clusters to detect associations between patterns, utilizing a network interface, processor, and memory to facilitate efficient analysis and correlation within big data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional data processing applications are used to analyze unstructured data from big data sources, then the analysis can be performed with simple tools, but the identification of common patterns becomes inefficient due to data complexity
Solution Approach 1:
The patent segments unstructured data into structured formats by extracting specific data elements and organizing them into standardized schemas. This segmentation transforms complex unstructured data into manageable structured components that can be efficiently analyzed for common patterns while maintaining tool simplicity.
2Ease of operation
If data elements are extracted from big data sources without correlation, then the extraction process is straightforward, but the search for additional useful data becomes inefficient
Solution Approach 1:
The patent implements feedback mechanisms where extracted data elements are correlated with previously extracted elements to identify relationships and patterns. This feedback loop enables the system to learn from extracted data and improve subsequent extraction efficiency, reducing time for searching additional useful data while maintaining ease of extraction.
3Device complexity
If unstructured data is analyzed without signature generation, then the analysis process is simpler, but the identification of common patterns among data elements becomes inefficient
Solution Approach 1:
The patent generates signatures that are simplified representations or copies of complex unstructured data elements. These signatures capture essential characteristics of the original data in a condensed format, enabling efficient pattern identification without requiring analysis of the full complexity of the original unstructured data.
Data Source
AI summary
A method for detection of common patterns within unstructured data elements. The method includes extracting a plurality of unstructured data elements retrieved from a plurality of big data sources; generating at least one signature for each of the plurality of unstructured data elements; identifying common patterns among the generated signatures; clustering the signatures identified to have common patterns; and correlating the generated clusters to identify associations between their respective identified common patterns.