Hash-Based Data Correlation for Mobile Network Optimization
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Solution Overview
Problem
Existing techniques for correlating large volumes of data in communication networks are inefficient, consuming substantial memory and processor resources, and are not well-suited to handle the increased data traffic from mobile devices, leading to performance optimization challenges.
Innovation Solution
A system comprising a data correlator and a data mapper that creates hash values to combine data packets with key elements, such as device network identifiers and user identifiers, to optimize network performance by correlating and combining data packets quickly and efficiently, minimizing memory and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing techniques for correlating large volumes of data are used, then data correlation can be performed, but substantial memory and processor resources are consumed and the process is time-consuming
Solution Approach 1:
The patent segments the data correlation process into distinct functional modules: a data receiver that collects data packets, a data processor that correlates the packets using hash-based indexing, and a data outputter that delivers results. This segmentation allows each module to be optimized independently and enables parallel processing, improving productivity while reducing overall resource consumption through efficient task distribution.
Solution Approach 2:
The patent transforms the data correlation approach by changing the parameter of data identification from direct comparison to hash value indexing. By converting data packets into hash values and using these as indices in a lookup table, the system achieves O(1) average time complexity for correlation operations, dramatically improving processing speed while reducing memory access requirements compared to traditional linear search methods.
2Loss of time
If existing techniques for correlating large volumes of data are used, then data correlation can be performed, but the process is time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing hash values of data packets in a lookup table before actual correlation is needed. When a data packet needs to be correlated, the system simply performs a hash computation and immediate table lookup, eliminating the need for time-consuming sequential comparisons. This preliminary preparation of hash indices reduces correlation time from O(n) to O(1) average case.
3Quantity of substance
If existing techniques for correlating large volumes of data are used, then data correlation can be performed, but substantial memory resources are consumed
Solution Approach 1:
The patent uses copying by creating hash value representations of data packets instead of storing and comparing the entire original data sets. The hash function compresses large amounts of data into fixed-size indices, allowing the system to handle large quantities of data (high data handling capacity) while using minimal memory resources for the lookup table. This copying approach maintains data integrity for correlation purposes while dramatically reducing memory footprint.
Data Source
AI summary
A system and method uses a data correlator system to associate large volumes of related data. The data correlator system receives data packets with user content from a mobile network. The data correlator system creates a hash value for data associated with the user content, such as user account information or the location of the user device. The data correlator system uses the hash value to combine the user content with the associated data. The data correlator system provides the combined data to an optimization engine for optimizing the performance of the mobile network.