Database Synchronization via Multi-Threaded API Processing
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Solution Overview
Problem
Current database synchronization processes between incompatible databases, such as activity and task databases, are manual, time-consuming, and prone to inaccuracies.
Innovation Solution
A database synchronization system that uses multiple processing threads to automatically and periodically synchronize data between activity and task databases by retrieving activity data, analyzing it, and updating task data through APIs, without user intervention, using participant IDs and domain IDs to modify entity and task objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual database synchronization is performed by data entry personnel, then data can be transferred between incompatible databases, but the process is time-consuming and yields incomplete and inaccurate results
Solution Approach 1:
The system enables self-service automation where the synchronization process executes automatically without human intervention. The computer system autonomously retrieves data from the first database, transforms it according to defined rules, and loads it into the second database, eliminating the need for manual data entry personnel and thereby resolving the contradiction between data accuracy and time consumption
Solution Approach 2:
The patent replaces the mechanical manual process of data entry with an automated computer-based system. The mechanical action of manual copying and pasting data is substituted with automated data retrieval, transformation, and loading operations executed by software, which significantly improves both speed and accuracy while eliminating human error
2Ease of operation
If manual synchronization processes are used, then data can be transferred between databases, but the process is complex and requires user intervention
Solution Approach 1:
The system is designed to execute synchronization operations autonomously without requiring user intervention during the actual data transfer process. The automated retrieval, transformation, and loading operations are performed by the computer system itself, simplifying the user experience while managing the inherent complexity of synchronizing incompatible databases
3Productivity
If automated synchronization is implemented, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The synchronization process is divided into distinct sequential stages: data retrieval from the first database, data transformation according to defined rules, and data loading into the second database. This segmentation allows the system to manage complexity through modular processing while maintaining high productivity through automated execution of each stage
Solution Approach 2:
The patent introduces an intermediate transformation stage where data is processed and converted according to defined rules before being loaded into the target database. This intermediary processing layer manages the complexity of incompatible database structures while enabling efficient automated synchronization between systems
Data Source
AI summary
Through a first processing thread, a first database is accessed via a first API to retrieve a list of event objects of the first database. Through a second processing thread, for each of the event objects, participant identifiers (IDs) are determined from the event object. For each of the participant IDs, a domain ID is extracted from the participant ID. A list of one or more entity objects are identified based on the domain ID, where the entity objects are stored in a second database such as a task database storing and managing many tasks. At least one attribute of at least one of the entity objects is modified based of the participant ID and the domain ID, which generates a modified entity object. Through a third processing thread, any event objects that have been modified are transmitted to the second database via a second API over the network.