Application Integration Queues for Server-Limited SaaS Data Extraction
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Solution Overview
Problem
Integrating SaaS applications in organizations is time-consuming and costly due to the need to configure numerous data fields and understand application-specific protocols, especially when a single entity must integrate information from multiple organizations.
Innovation Solution
A computing system and method that utilizes a data extractor with access to application-specific rules, managing jobs through queues with limitations, and generating jobs based on integration plans to automate the integration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual integration processes are used for each application, then integration completeness can be achieved, but integration time and costs increase significantly
Solution Approach 1:
The integration process is segmented into standardized job types (data extraction, transformation, loading) that can be independently configured and executed. Each application integration is divided into discrete tasks that follow a common framework, allowing parallel processing and reuse of integration patterns across multiple applications.
Solution Approach 2:
Integration templates and job configurations are prepared in advance before actual data extraction begins. The system pre-defines extraction rules, transformation logic, and target schema mappings, so that when integration is triggered, execution can proceed rapidly without ad-hoc configuration for each application.
2Manufacturing precision
If more data fields are extracted from applications, then integration completeness improves, but server limitations and processing complexity increase
Solution Approach 1:
The system applies different extraction and processing rules to different data fields based on their specific characteristics. Each data field can have customized extraction logic, transformation rules, and validation requirements, allowing the system to handle diverse data types and formats with appropriate local化处理 while maintaining overall process standardization.
Solution Approach 2:
The system dynamically adjusts extraction parameters, batch sizes, and processing depth based on server capacity, data volume, and priority requirements. Configuration parameters control the level of detail extracted from each application, enabling flexible trade-offs between completeness and resource consumption without hardcoding complex processing logic.
3Manufacturing precision
If integration processes are customized for each application, then integration accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The system employs universal integration templates and standardized job types that can be applied across multiple different applications. A single template can be reused for extracting user data, organizational data, or activity data from various SaaS platforms, reducing the need for custom development while maintaining application-specific accuracy through configurable parameters.
Solution Approach 2:
The patent introduces an intermediary layer of abstraction between the diverse application sources and the target data warehouse. This intermediary integration layer handles application-specific protocols and data formats, translating them into a unified internal representation that simplifies operations while preserving the accuracy needed for each specific application type.
4Productivity
If multiple applications are integrated simultaneously, then productivity improves, but server limitations and job management complexity increase
Solution Approach 1:
The system segments the integration workload into independent job queues that can be processed concurrently. Each application integration is divided into discrete tasks (extract, transform, load) that can be executed in parallel across multiple servers, maximizing throughput while maintaining manageable job units that can be monitored and controlled individually.
Solution Approach 2:
The system maintains continuous integration operations across multiple applications by implementing persistent job queues and background processing. Rather than batch-processing all integrations sequentially, the system continuously extracts and transforms data from multiple applications simultaneously, keeping integration pipelines active and productive without idle time between jobs.
Data Source
AI summary
A method for integrating an organization into a computer application by generating jobs to request data from the application servers according to an integration plan for the computer application, assigning queue IDs to jobs required to be performed as part of the integration plan, where each queue ID of the queue IDs is associated with multiple jobs that share a limitation of the application's servers executing the jobs at the relevant queue, executing the jobs in the integration plan in queues according to the queue IDs, and updating jobs to a time-dependent list, where the update includes adding and removing jobs.