Probabilistic Latent Model for Shared Device Usage Correlation
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Solution Overview
Problem
Existing methods for managing shared devices often focus either on device-centric or user-centric views, neglecting correlations between user attributes and job classes, which limits their ability to analyze and optimize shared device usage effectively.
Innovation Solution
A probabilistic latent model is used to analyze recorded job data, characterizing each job with observed variables for users and devices, and latent variables for job clusters and service classes, allowing for the discovery of device usage communities and user behavior patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If device-centric or user-centric views are used to analyze shared device usage, then the analysis is simple and easy to implement, but the analysis fails to capture correlations between user attributes and job classes, limiting its effectiveness
Solution Approach 1:
The analysis is segmented into multiple dimensions: device-centric view, user-centric view, and job-class view. Each dimension captures specific aspects of usage data, and the probabilistic model integrates these segmented views to recover correlation information that would be lost in single-perspective analyses.
Solution Approach 2:
The probabilistic latent model serves multiple functions simultaneously: it models device usage patterns, user behavior, job class distributions, and the correlations between these elements. This multi-functional approach allows the system to capture rich correlation information while maintaining a unified analytical framework.
2Loss of information
If a probabilistic latent model with multiple latent variables is used to analyze shared device usage, then correlation between users and devices is captured, but the model complexity increases
Solution Approach 1:
The model introduces latent dimensions (job clusters and service classes) that are not directly observable but provide explanatory power. By moving from observed variables (user, device) to latent variables (job cluster, service class) and back, the model captures correlations without requiring explicit correlation parameters, managing complexity through dimensional transformation.
Solution Approach 2:
Latent variables act as intermediaries between observed user and device variables. These latent job cluster and service class variables mediate the relationship between users and devices, allowing the model to capture correlations through the intermediary structure rather than direct parameterization.
3Measurement precision
If comprehensive usage data is collected and analyzed to discover user behavior patterns, then the accuracy of behavior modeling improves, but the processing time and computational resources increase
Solution Approach 1:
The model uses partial action by focusing on the most informative aspects of usage data (user-device-job correlations) rather than processing all possible data dimensions. The probabilistic model selectively extracts meaningful patterns from comprehensive data, achieving high accuracy while reducing processing time through targeted analysis.
Data Source
AI summary
Methods are disclosed for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs. In operation, usage job data of observed users and devices carrying out the jobs is recorded. A probability model is defined with an observed user variable, an observed device variable, a latent job cluster variable, and a latent job service class variable. A range of job service classes associated with the shared devices is determined, and an initial number of job clusters is selected. Parameters of the probability model are learned using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters. The learned parameters of the probability model are applied to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.