Data Abbreviation Using ML Datapoints for Duplicate Storage Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Communication systems face challenges in storing, managing, and analyzing unstructured data due to its lack of organization, leading to redundant copies in databases.
Innovation Solution
A system and method utilizing machine learning models to dynamically abbreviate data by identifying and storing unique datapoints representative of unstructured and structured data, leveraging quantum registers and containerized clustering to reduce redundancy and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full unstructured data is stored in databases, then data completeness is maintained, but memory usage and storage resources increase significantly
Solution Approach 1:
The system extracts only the essential identifying features (datapoints) from complete unstructured data objects. For images, this means extracting key visual characteristics rather than storing entire image files. For text, this means extracting semantic identifiers rather than full text content. This extraction principle resolves the contradiction by maintaining data representability while dramatically reducing storage requirements.
Solution Approach 2:
Instead of copying complete data objects, the system creates simplified representative copies (datapoints) that capture the essential identity and characteristics of the original data. These datapoint copies are sufficient for identification, deduplication, and analysis purposes without requiring the full original data, thus reducing memory usage while preserving data completeness at the representative level.
2Reliability
If redundant copies of unstructured data are stored to ensure data availability, then data accessibility is improved, but processor and memory usage increase
Solution Approach 1:
The system extracts only the necessary identifying datapoints from complete data objects for storage and comparison purposes. These extracted features are sufficient for determining data uniqueness and availability without requiring redundant storage of complete data objects. This resolves the contradiction by maintaining data availability through efficient representation while reducing computational resources needed for processing and storing redundant copies.
3Measurement precision
If complete unstructured data is analyzed to prevent duplicates, then duplicate detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts key identifying features (datapoints) from complete unstructured data objects for duplicate detection purposes. These extracted features capture the essential characteristics needed to identify duplicates without requiring analysis of the entire data object. This resolves the contradiction by maintaining duplicate detection accuracy through representative feature extraction while significantly reducing processing time and computational resources.
Solution Approach 2:
Instead of performing complete data analysis, the system performs partial analysis by examining only the extracted datapoints. This partial action is sufficient for duplicate detection purposes and avoids the excessive computational burden of analyzing complete unstructured data objects, thus resolving the contradiction between detection accuracy and processing efficiency.
Data Source
AI summary
A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a request to store received data, determine whether the received data comprises unstructured data, evaluate the unstructured data of the received data in accordance with a machine learning algorithm in response to determining that the received data comprises the unstructured data, and perform an analysis operation to identify datapoints in the received data in response to evaluating the unstructured data of the received data. The datapoints may be a signature representation of the unstructured data of the received data. Further, the at least one processor may be configured to generate a roadmap to store the datapoints and store the datapoints following the roadmap. The roadmap is a plan to store the datapoints in the memory in accordance with one or more quantum random number generator (QRNG) operations.