Mobile Device Predictive Modeling Server-Client Workload Segmentation
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Solution Overview
Problem
Mobile devices lack the computational resources to efficiently execute complex predictive modeling processes, such as weather forecasting and investment projections, due to the high demand for computing capacity and resources, leading to hardware and software errors.
Innovation Solution
A system and method that manage predictive modeling processes between a server and a mobile device, optimizing resource allocation by determining suitable processing parameters, modifying simulation techniques like Monte Carlo simulations with trend reversion and current price deflection bias, and allocating processing tasks between the server and client device to accommodate the mobile device's capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex predictive modeling processes are executed on mobile devices, then predictive modeling capability is improved, but computational resource demands exceed mobile device capabilities causing hardware and software errors
Solution Approach 1:
The system divides the predictive modeling workload into segments: complex computations are performed on the server while the mobile device handles data input, intermediate results processing, and result display. This segmentation allows the mobile device to participate in predictive modeling without being overwhelmed by the full computational burden.
Solution Approach 2:
The server acts as an intermediary between the mobile device and the computational requirements of predictive modeling. The server receives modeling parameters from the mobile device, performs the computationally intensive simulations, and returns results to the mobile device, thereby protecting the mobile device from direct exposure to excessive computational demands.
2Measurement precision
If the number of calculations is increased to produce a full range of outcomes, then predictive modeling accuracy is improved, but computational resources are overwhelmed causing errors
Solution Approach 1:
The system performs a sufficient number of calculations to achieve acceptable predictive accuracy without attempting to exhaustively explore every possible outcome. The Monte Carlo simulation uses a predetermined number of iterations that balances accuracy requirements with mobile device computational limitations.
Solution Approach 2:
The system adjusts computational parameters such as the number of simulation iterations, random seed values, and convergence criteria to optimize the balance between predictive accuracy and computational resource consumption on mobile devices.
3Adaptability or versatility
If Monte Carlo simulations are performed to evaluate probability of outcomes, then predictive modeling capability is improved, but computational time and resources increase significantly
Solution Approach 1:
The system performs preliminary assessments to determine whether the mobile device has sufficient computational resources and time availability before initiating Monte Carlo simulations. If conditions are not favorable, the system adjusts the simulation parameters or defers execution to a server environment.
Solution Approach 2:
The Monte Carlo simulation is executed in periodic intervals or batches rather than as a continuous exhaustive process, allowing the mobile device to perform computations during available time windows and return results when computational tasks are complete.
Data Source
AI summary
Embodiments provide mobile computing devices, related systems, and methods for controlling predictive modeling processes on a mobile device. An embodiment may provide a method for controlling predictive modeling processes on a mobile device, which may include receiving modeling data associated with a predictive model, receiving computation resources data of the mobile device, determining one or more predictive model processing parameters of the predictive model based on the modeling data and the computation resources data, formulating processing instructions based on the one or more predictive model processing parameters, and sending the modeling data and the processing instructions to the mobile device for execution of the predictive model. The parameters may include aspects of a sufficient number of tests, a type of predictive modeling simulation technique, client/server processing, and/or device responsive rendering.