Dynamic Mobile User Data Storage Optimization
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Solution Overview
Problem
The challenge lies in efficiently storing large amounts of mobile user data without incurring excessive power consumption, cooling requirements, noise, administrative costs, and data recovery costs, while still gaining meaningful insights into user activities, experiences, and preferences.
Innovation Solution
A system and method for optimizing mobile user data storage by adjusting parameters such as data type, collection frequency, and event triggers, allowing for dynamic adjustment of data collection based on user location and activity, thereby reducing storage requirements while maintaining sufficient data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If more mobile user data is received and stored for analysis, then more insight is provided as to the mobile user's activities, experiences, preferences, and the like, but power consumption increases
Solution Approach 1:
The patent extracts and stores only the most valuable and frequently accessed mobile user data in hot storage, while less frequently accessed data is moved to cold storage. This selective extraction approach maintains user insight capabilities while significantly reducing the energy required for data storage and processing.
Solution Approach 2:
The patent segments mobile user data into different storage tiers (hot storage and cold storage) based on access frequency and value. This segmentation allows the system to apply different energy consumption characteristics to different data portions, reducing overall power consumption while preserving analytical insights.
2Loss of information
If more mobile user data is received and stored for analysis, then more insight is provided as to the mobile user's activities, experiences, preferences, and the like, but cooling requirements increase
Solution Approach 1:
The patent extracts only the essential and frequently accessed data elements for active analysis, removing less critical data from the hot storage tier. This reduction in stored data volume directly decreases the thermal load and cooling requirements of the storage system.
Solution Approach 2:
By segmenting data into hot and cold storage tiers, the patent concentrates computational and thermal resources on only the necessary data subset, reducing the overall cooling infrastructure required compared to storing all mobile user data uniformly.
3Loss of information
If more mobile user data is received and stored for analysis, then more insight is provided as to the mobile user's activities, experiences, preferences, and the like, but administrative costs increase
Solution Approach 1:
The patent automatically extracts and identifies the most valuable mobile user data for storage using algorithms that assess data utility and access patterns. This automated extraction reduces the need for manual data cur culation and administrative overhead in managing stored data.
Solution Approach 2:
The automated segmentation of data into storage tiers based on analytical value reduces administrative complexity by implementing clear data lifecycle management policies, reducing costs associated with data governance, compliance, and storage management.
4Loss of information
If more mobile user data is received and stored for analysis, then more insight is provided as to the mobile user's activities, experiences, preferences, and the like, but disaster and data recovery costs increase
Solution Approach 1:
The patent extracts and prioritizes storage of critical mobile user data that provides the most analytical insight, while implementing selective backup and recovery strategies for different data tiers. This approach reduces disaster recovery costs by focusing recovery resources on the most valuable data.
Solution Approach 2:
By segmenting data into different storage tiers with different protection levels, the patent implements a risk-based data recovery strategy where critical data receives enhanced protection and recovery capabilities, while less critical data uses more cost-effective recovery methods, overall reducing disaster recovery costs.
Data Source
AI summary
Systems and methods described herein provide a mechanism for optimizing the reception and storage of mobile user data. Data relating to mobile user location, behavior, and profiles are received from mobile users and stored at a storage system in an efficient manner. The efficiency is achieved by identifying what system parameters may be adjusted to reduce storage requirements while still providing sufficient data for useful analysis. System parameters that may change to reduce storage requirements include, e.g., the type of mobile user data collected, the frequency at which mobile user data is collected, the events or conditions that trigger data collection, and dynamically adjusting data collection upon detecting a number of event or time-based triggers.