Tuple Clustering for Dynamic Unstructured Content Threads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Unstructured databases lack efficient query processing capabilities due to their unruly nature, making it difficult to identify and cluster significant content effectively.
Innovation Solution
A method involving the segmentation of datasets by temporal or logical criteria, formation of baseline and target tuples, coalescing these tuples based on criteria to form a coalesced dataset, and integrating updated content to cluster documents into threads of related content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional query processing methods are used on unstructured databases, then the database structure is maintained, but query efficiency and content identification capability deteriorate
Solution Approach 1:
The patent segments the unstructured database into structured components by dividing data into tuples (subject, object, relationship, timestamp) and organizing them into coalesced datasets. This segmentation transforms the unruly unstructured data into manageable, queryable units while preserving the original data integrity, thereby improving both query efficiency and content identification capability
Solution Approach 2:
The patent introduces an intermediary processing layer that converts unstructured data into a semi-structured tuple format. This intermediary representation serves as a bridge between the unstructured database and traditional query processing systems, enabling efficient querying without requiring complete database restructuring
2Measurement precision
If the entire baseline dataset is processed to identify significant content, then comprehensive analysis is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by processing only the necessary portions of the baseline dataset to identify significant content. Instead of analyzing every document equally, the system uses coalescing operations to combine and deduplicate tuples, focusing computational effort on identifying unique relationships and patterns rather than redundantly processing identical information
Solution Approach 2:
The patent performs preliminary processing by pre-segmenting the baseline dataset into tuples and pre-identifying significant content patterns before final querying. This preliminary organization of data into coalesced datasets reduces the computational burden during actual query operations, as the heavy lifting of data structuring has already been completed
Data Source
AI summary
A computer implemented method includes receiving a baseline dataset divisible by temporal or logical criteria. A target dataset representing a small fraction of the baseline dataset is received. The target dataset is segmented by the temporal or logical criteria. Numbers of documents containing individual words within the baseline dataset are identified to form baseline singles. Numbers of documents containing common combinations of individual words within the target dataset are identified to form target tuples. Combinations of the target tuples are coalesced based upon baseline singles criteria to form a coalesced dataset representing significant content in the target dataset. The coalesced dataset is used to cluster documents from the target dataset into threads of related content. Updated content is received. The updated content is integrated with the coalesced dataset to form updated threads of related content.