Mobile Data Replication via Local Storage and Scheduled Sync
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Solution Overview
Problem
Conventional systems for data replication among mobile devices incur high network charges, experience latency issues, and face challenges in seamless upgrades and offline data manipulation, leading to negative user experiences and battery life degradation.
Innovation Solution
A system where mobile devices store relational data locally and post changes to a central server, which schedules data updates based on user activity, using a central relational database management system to host multiple database applications and communicate via JSON documents, allowing for efficient data replication and seamless upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is stored and shared via a central server over wireless networks, then data sharing is enabled, but users incur high network charges and experience latency
Solution Approach 1:
The system segments data storage and processing functions between mobile devices and central servers. Each mobile device maintains local storage for its data, eliminating the need for continuous network communication for basic data access and manipulation. Only data synchronization operations require network communication, significantly reducing network charges and latency.
Solution Approach 2:
The system performs preliminary data storage and processing actions locally on mobile devices before network communication is needed. Users can manipulate, view, and share their data offline immediately without waiting for server responses, and only synchronize changes when network connectivity is available.
2Ease of operation
If data is stored locally on mobile devices, then offline data manipulation is enabled, but data replication and sharing become inconsistent
Solution Approach 1:
The system implements a feedback mechanism where mobile devices automatically detect and transmit data changes to the central server when network connectivity is available. The server then propagates these changes to other devices in the network, ensuring all devices receive updates and maintain consistent data states through continuous feedback loops.
Solution Approach 2:
The system performs preliminary local storage and manipulation of data on mobile devices, allowing offline access and editing. Change tracking mechanisms record all modifications locally, and synchronization protocols automatically resolve conflicts and propagate changes to maintain consistency across the distributed network when connectivity is restored.
3Adaptability or versatility
If application upgrades are deployed on mobile devices, then functionality is improved, but devices may freeze or reboot during updates
Solution Approach 1:
The system downloads application upgrade packages to the device's local storage in advance, allowing users to review and approve updates before installation. The actual installation is scheduled for appropriate times when the device is idle, preventing interruptions during critical operations like phone calls.
Solution Approach 2:
The system dynamically schedules application upgrades based on device usage patterns and network conditions. Upgrades are automatically deferred if the device is actively being used, and only installed during idle periods when no critical operations are running, ensuring device stability while maintaining functionality improvements.
Data Source
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AI summary
System, methods and articles of manufacture for replicating relational data on multiple nodes. An embodiment comprises receiving an update request message from a node, wherein the update request message comprises a node identification and an application identification, accessing an application schema based on the application identification, identifying a partition residing in the application schema based on the node identification, accessing a server schema, identifying a second partition residing in the server schema based on the application identification, determining at least one data change stored in the second partition that changes data associated with the first partition, retrieving at least one data change from the second partition, formatting data change in an update response message, and transmitting the update response message to the node.