Lightweight Word-Based Scheduler for Mobile Devices
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Solution Overview
Problem
Current scheduling systems face challenges in efficiently processing large datasets across multiple databases, leading to high processing costs and time, which limits real-time scheduling capabilities, especially in disconnected or mobile modes, and lack on-demand support.
Innovation Solution
Implementing a lightweight word-based structure for scheduling data that can be loaded onto mobile devices, allowing for real-time and on-demand scheduling, enabling scheduling in disconnected mode with subsequent synchronization with the server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional database structures with multiple tables are used for scheduling data, then data completeness and relationships are maintained, but processing time and memory requirements increase significantly
Solution Approach 1:
The patent combines multiple separate database tables (calendaring database, language abilities database, technical training database) into a single integrated data structure. This merging eliminates the need for costly join operations across multiple tables, significantly reducing processing time while maintaining all necessary scheduling data in one unified structure.
Solution Approach 2:
The unified data structure serves multiple functions simultaneously: it stores calendar information, language abilities, technical training data, and other scheduling-relevant information. This multi-functional structure replaces multiple specialized databases, reducing processing overhead while maintaining data completeness for various scheduling queries.
2Quantity of substance
If extensive database infrastructure is used to store scheduling data, then data storage capacity is sufficient, but device complexity and connectivity requirements increase
Solution Approach 1:
The patent consolidates what would traditionally require multiple separate database systems into a single unified data structure that can be implemented on mobile devices. This reduction in infrastructure complexity enables scheduling operations to be performed locally without requiring connections to extensive centralized database systems.
3Measurement precision
If batch processing is used to handle large scheduling datasets, then processing completeness is achieved, but real-time scheduling capability is lost
Solution Approach 1:
By merging all scheduling data into one unified structure, the patent enables the entire dataset to be loaded into memory and processed in real-time. This eliminates the need for batch processing of large datasets, as the integrated structure allows for rapid querying and scheduling decisions to be made immediately based on current availability and requirements.
4Measurement precision
If multiple database join operations are performed to retrieve scheduling data, then data accuracy is maintained, but processing cost and memory usage increase
Solution Approach 1:
The patent merges data from multiple source tables into a single unified structure, eliminating the need for multiple join operations. This reduces processing cost and memory usage while maintaining data accuracy, as all required information is already integrated in the unified structure and can be retrieved through simple queries without costly join operations.
Data Source
AI summary
Disclosed is an improved approach for implementing an on-demand scheduler in a mobile device and the structures to support realtime on-demand schedulers. A lightweight word-based structure is disclosed for storing scheduling-related data on the mobile device. Using this lightweight word-based structure enables on-demand and real-time scheduling. This type of lightweight structure also permits scheduling activities to be performed in a disconnected mode, which can then be later synchronized with the server to confirm the booking In addition to appointment scheduling, this technique can also be implemented for scheduling of any type of resource.