Application Quality Index for SaaS Multi-Tenant Integration
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Solution Overview
Problem
Current SAAS systems lack the ability to effectively determine the quality of integration of multi-tenant applications, as they struggle to aggregate and normalize complex historical data, hindering improvements in application integration with the SAAS system.
Innovation Solution
A processor-based system generates an Application Quality Index (AQI) by processing data from various sources, assigning weighted values to sub-indexes such as customer retention, API performance, and user experience, to rank applications and suggest areas for improvement, leveraging statistical learning models to dynamically determine weighted values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SAAS systems aggregate complex historical data to determine integration quality, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex data aggregation task into distinct functional modules: data collection module that gathers integration events, data processing module that normalizes and cleanses data, analysis module that computes quality metrics, and reporting module that presents results. This segmentation allows each module to handle specific aspects of data processing independently, reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces intermediary components including data normalization layers that standardize disparate data formats, buffering mechanisms that temporarily store and manage data flows, and abstraction layers that simplify complex queries. These intermediaries act as mediators between raw historical data and quality metrics, reducing the complexity burden on the core analysis system.
2Loss of information
If SAAS systems collect and process extensive historical data, then information completeness improves, but loss of time increases
Solution Approach 1:
The patent implements preliminary data normalization and validation processes that prepare historical data in advance for analysis. Data is cleansed, standardized, and pre-aggregated into manageable formats before quality assessment begins, reducing the time required for actual quality computation while ensuring complete information is retained.
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant historical data subsets needed for specific quality metrics, rather than analyzing every available data point. Excessive action is used by maintaining pre-computed data aggregates and summaries that can be quickly queried, providing near-complete information without processing entire historical datasets.
3Productivity
If SAAS systems provide detailed quality metrics, then productivity improves, but device complexity increases
Solution Approach 1:
The patent provides local quality by offering different levels of quality metric detail tailored to specific user needs and contexts. Developers can access comprehensive detailed metrics when needed for debugging and optimization, while getting summarized overview metrics for routine monitoring. This localized quality approach delivers high productivity support without requiring the system to always maintain maximum complexity.
Solution Approach 2:
The patent implements dynamic quality reporting that adapts the level of detail and complexity based on user roles, application contexts, and interaction history. The system dynamically adjusts which metrics are displayed, their granularity, and the depth of analysis provided, delivering high productivity value while managing system complexity through adaptive behavior rather than static comprehensive design.
Data Source
AI summary
A computer-implemented method for determining an Application Quality Index (AQI) Score of a software-as-a-service (SAAS) multi-tenant application to a SAAS system is provided. The method may be performed on a computing device executing a processor. The method may include accessing data at a data repository. The accessed data includes at least connection/disconnection data and API error data of the SAAS multi-tenant application. The method also includes processing the accessed data to generate two or more sub-indexes. The two or more sub-indexes includes at least a first sub-index based on the connection/disconnection data and a second sub-index based on the API error data. The first sub-index is assigned a weighted value based on content of the connection/disconnection data and the second sub-index is assigned a weighted value based on content of the API error data. The method also includes generating the AQI based on the weighted value of the two or more sub-indexes, sending the generated AQI to the SAAS multi-tenant application, and receiving an update to the SAAS multi-tenant application based on the generated AQI.