Synthetic Data Generation Using Markov Chain Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating synthetic data records in telecommunication networks fail to efficiently preserve user privacy and accurately model actual network distributions, particularly when distributing samples between user clusters.
Innovation Solution
A method and system that utilize Markov Chains to randomly select state transitions and context variables based on their distributions, perform random walks on graph models, and generate synthetic data records that capture cause-effect conditions between variables, while ensuring context independency and privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Markov Chains are used to generate synthetic time series data, then statistical properties are preserved, but sample distribution between user clusters is not maintained
Solution Approach 1:
The patent segments users into distinct clusters based on their behavior patterns and generates synthetic data for each cluster separately. This segmentation allows the system to maintain both statistical properties within clusters and proper sample distribution across clusters, resolving the contradiction by treating different user groups as independent segments that can be modeled with appropriate Markov Chains.
Solution Approach 2:
The patent applies local quality by using context-specific Markov Chains for different user clusters and contexts. Each cluster receives a customized model that reflects its specific statistical properties and transition patterns, rather than applying a single global model. This ensures both local statistical accuracy and global distribution fidelity.
2Loss of information
If real user data is used for analysis, then accurate network insights are obtained, but user privacy is compromised
Solution Approach 1:
The patent creates synthetic copies of user data that replicate the statistical properties, transition patterns, and behavioral characteristics of real user data without containing actual personal information. These synthetic copies can be freely analyzed for network insights while user privacy remains protected, as the synthetic data cannot be traced back to individual users.
Solution Approach 2:
The synthetic data generation system acts as an intermediary between real user data and analysis applications. Instead of directly accessing sensitive real data, analysts work with synthetic intermediaries that preserve the essential patterns and relationships needed for accurate network insights while eliminating privacy risks.
3Productivity
If synthetic data is generated without cluster distribution, then generation speed is improved, but modeling accuracy of actual networks decreases
Solution Approach 1:
The patent performs preliminary actions by pre-segmenting users into clusters and pre-training context-specific Markov Chains for each cluster before generating synthetic data. This preliminary organization allows the generation process to proceed efficiently by simply sampling from pre-computed models while maintaining accurate cluster distribution, thus achieving both speed and accuracy.
Data Source
Figure 1
AI summary
A method for generating synthetic data records which include datasets that capture state-based transitions, according to which a state transition family is randomly selecting, according to the distribution of samples between the different clusters of users and the context variables are randomly sampled according to their distribution within the chosen cluster. The relevant Markov Chains models are selected according to the sampled context and the initial state of the sequence is randomly selected according to the distribution of states. A random walk process is initialized on the graph models and the random walk is performed process on each context separately, assuming context independency. The cause condition of the current transition is sampled for each state transition, based on the distributions on the selected edge.