Dynamic Data Collection Pattern for Target Devices
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Solution Overview
Problem
Conventional data collection systems face inefficiencies due to static data collection rules, which are often designed for the lowest-end device resources to avoid performance issues, leading to excessive time and cost in creating rules for various device types, and inefficient data handling across multiple devices.
Innovation Solution
A dynamic data collection pattern modification system that adjusts data collection rules based on the target device's resources and capabilities, using a telemetry module with a scaling profile manager to optimize data collection and analysis, enabling real-time scaling and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static data collection rules are designed for lowest-end device resources, then performance issues on low-end devices are avoided, but data collection efficiency and resource utilization deteriorate on higher-end devices
Solution Approach 1:
The patent implements dynamic data collection rules that automatically adjust collection frequency, data volume, and collection items based on the device's actual performance parameters (CPU speed, memory size, storage capacity). This transforms the static, one-size-fits-all approach into a dynamic system that adapts to each device's capabilities, thereby maintaining performance stability on low-end devices while maximizing data collection efficiency on higher-end devices.
Solution Approach 2:
The system changes key parameters of data collection (collection frequency, data volume, collection items) based on device performance parameters. By establishing a mapping between device capabilities and collection parameters, the system optimizes resource utilization across different device types without requiring manual rule design for each device category.
2Adaptability or versatility
If multiple static rules are designed for different device types, then data collection can be optimized for various device profiles, but development time and cost increase excessively
Solution Approach 1:
The patent enables the data collection system to automatically determine appropriate collection rules by having the device report its own performance parameters (CPU, memory, storage) and the system automatically selecting or generating suitable collection parameters. This self-service mechanism eliminates the need for developers to manually create and maintain multiple static rule sets for different device types, significantly reducing development time and cost while maintaining broad device compatibility.
Solution Approach 2:
Instead of creating separate rules for each device type, the system uses a parameter-based approach where collection rules are defined in terms of device performance thresholds. The system automatically adjusts collection parameters based on the device's actual specifications, providing versatile device type coverage through a single adaptable rule set rather than multiple static rules.
3Use of energy by moving object
If data collection is optimized for low-end devices, then resource consumption is minimized, but data volume and analysis quality decrease
Solution Approach 1:
The system dynamically adjusts data collection parameters (frequency, volume, granularity) based on device performance capabilities. Devices with higher CPU speed, more memory, and larger storage capacity automatically receive higher data collection parameters, enabling full-quality data collection on powerful devices while maintaining resource efficiency on constrained devices. This eliminates the information loss that occurs when low-end optimized rules are applied to capable devices.
Data Source
AI summary
A telemetry module integrated with an application may include a data collection and analysis engine configured to implement a data collection pattern comprised of data collection rules to perform data collection and analysis for reporting to a service associated with the application. The telemetry module may also include a scaling profile manager configured to dynamically scale the data collection and analysis performed by the data collection and analysis engine for a target device such that parameters of the data collection and analysis correspond to resources and capabilities of the target device. After scaling, the data collection and analysis engine may then be further configured to determine additional data collection rules based on the scaled data collection and analysis, and dynamically modify the data collection pattern implemented based on the additional data collection rules such that the data collection pattern also corresponds to resources and capabilities of the target device.