Data Segment Clustering for Rapid Retrieval
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Solution Overview
Problem
Large data sets, such as those exceeding a terabyte, pose challenges in quick data retrieval due to the time-consuming process of serially reading stored data, making it difficult to search for patterns of interest efficiently.
Innovation Solution
A system with an input port, output port, buffer, and controller that analyzes a data stream by identifying and comparing segments to reference data segments using similarity protocols, creating new segments and combining or compressing them as necessary, allowing for real-time clustering and efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored on conventional disk drives, then large data sets can be recorded, but the time to read and search the data becomes several hours
Solution Approach 1:
The patent divides the large data set into many small data segments (e.g., 1000-byte segments) that can be independently processed and searched. This segmentation allows the system to quickly locate and retrieve only relevant segments rather than reading through entire terabytes of data sequentially, dramatically reducing search time while maintaining full storage capacity.
Solution Approach 2:
The system performs preliminary actions by pre-processing data during the recording phase - organizing data into standardized segments, creating segment databases, and establishing segment relationships before the actual search is needed. This pre-organization enables rapid retrieval during search operations without requiring full data reprocessing.
2Ease of operation
If data is read serially from stored data, then all data can be accessed, but the search process becomes time-consuming
Solution Approach 1:
The patent introduces a new dimensional approach to data access by creating a segment database that indexes data segments independently from their original sequential positions. This allows the system to access data segments in random order based on search criteria rather than following the original sequential read path, effectively adding an indexing dimension to the data access structure.
Solution Approach 2:
The system creates copies of data segment information in segment database structures that contain metadata about each segment (position, content characteristics, relationships). These copies enable the search function to operate on the segmented data structure rather than the original full data set, allowing parallel processing and faster identification of relevant data without compromising complete data accessibility.
3Productivity
If the system processes large data sets in real-time, then data analysis can be performed quickly, but the processing complexity increases
Solution Approach 1:
The patent applies segmentation to break down the complex task of real-time data analysis into simpler sub-tasks: segmenting incoming data streams into manageable units, segmenting processing operations into extraction/comparison/storage phases, and segmenting results into grouped categories. This modular approach enables real-time processing while keeping individual processing components relatively simple.
Solution Approach 2:
The system performs preliminary data processing actions during data ingestion - immediately segmenting incoming data, extracting relevant segments, and organizing them in segment databases before full analysis is required. This pre-processing reduces the complexity of subsequent real-time analysis operations by having data already organized and indexed when analysis begins.
Data Source
AI summary
A system for recording and analyzing a data stream, a method for analyzing a data stream, and a computer readable memory that stores instructions that cause a computer to execute a method of analyzing a data stream are disclosed. The system includes an input port, output port, buffer, and controller. The controller identifies a segment, referred to as a new extracted data segment (EDS) of the data stream stored in a buffer, the new EDS satisfying an extraction protocol. The controller compares the new EDS to each of a plurality of reference data segments (RDSs) using a similarity protocol. A new RDS is created if the new EDS is not similar to an existing EDS. If the new EDS is similar to an RDS, the RDS is updated to list that new EDS as being similar.