Data Collection Server Using Use Frequency Estimation for Storage Allocation
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Solution Overview
Problem
The existing data circulation market lacks techniques for effectively handling massive amounts of device data generated by advanced Internet of Things (IoT) technologies, particularly in terms of storage and utilization.
Innovation Solution
A data collection server is designed with reception, use frequency estimation, and saving mechanisms to categorize and store device data based on its estimated use frequency across multiple storage levels, optimizing storage and retrieval processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all device data is stored in high-performance storage, then data accessibility and retrieval speed are improved, but storage costs and resource consumption increase significantly
Solution Approach 1:
The patent segments storage resources into multiple levels (first storage means with high performance, second storage means with lower performance) and categorizes device data into different groups based on use frequency. Frequently accessed data is stored in the first storage means while less frequently accessed data is stored in the second storage means, thereby resolving the contradiction between retrieval speed and storage cost.
Solution Approach 2:
The patent applies local quality by assigning different storage qualities to different data based on their specific access patterns. High-use-frequency data receives high-performance storage allocation while low-use-frequency data is allocated to lower-performance storage, optimizing the overall system performance-to-cost ratio.
2Quantity of substance
If data is stored in multiple storage levels, then storage cost efficiency is improved, but data management complexity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically estimate use frequency of device data and autonomously determine optimal storage allocation without requiring manual intervention. The data collection server performs use frequency estimation and automatically stores data in appropriate storage levels, reducing management complexity while maintaining cost efficiency.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors data access patterns, estimates use frequency, and dynamically adjusts storage allocation. This feedback loop enables automatic optimization of storage resource allocation based on actual data usage, simplifying management while improving cost efficiency.
3Quantity of substance
If use frequency estimation is performed for all device data, then storage optimization is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing use frequency estimation selectively rather than uniformly on all device data. The system estimates use frequency for data that benefits from optimized storage allocation while using default or simplified storage for other data, thereby reducing processing overhead while maintaining storage optimization benefits.
Data Source
AI summary
A data collection server includes a communicator, a use frequency estimator, and a data saver. The communicator receives device data from a device. The use frequency estimator estimates use frequency of the device data received by the communicator. The data saver stores the device data in, among a plurality of storage servers corresponding to different levels of use frequency, one of the plurality of storage servers that corresponds to the use frequency estimated by the use frequency estimator.