Automated QoE Evaluation via Table-Driven SQL Generation
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Solution Overview
Problem
Existing network measurement systems require time-consuming and error-prone manual edits to update and add new metrics for measuring network performance, especially with advancements in technologies like 5G networks, making it challenging to accurately track key performance indicators and quality of experience (QoE) for wireless communication services.
Innovation Solution
Automating the generation of structured query language (SQL) statements for calculating key quality indicators (KQIs) and quality of experience (QoE) using table-driven approaches, which involves mapping performance indicators to sub-key quality indicators (SKQIs) and SKQIs to KQIs, allowing for the automatic calculation and storage of these metrics, reducing human error and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual edits are used to update and add new metrics for measuring network performance, then flexibility in customizing metrics is improved, but time consumption and error rate increase
Solution Approach 1:
The system performs automatic generation of SQL statements and calculation of QoE metrics without requiring manual intervention. The automated system serves itself by reading mapping tables and generating the necessary computational logic, eliminating the time-consuming manual edit process while maintaining flexibility through configurable mapping tables.
Solution Approach 2:
The mapping tables (KPI to SKQI mapping table and SKQI to QoE mapping table) are prepared in advance, defining all relationships and calculation logic before execution. This preliminary configuration allows the system to automatically generate SQL statements and compute metrics without requiring real-time manual adjustments, thus reducing time consumption while maintaining adaptability.
2Adaptability or versatility
If manual edits are used to update and add new metrics for measuring network performance, then flexibility in customizing metrics is improved, but error rate increases
Solution Approach 1:
The automated system eliminates human error by performing all metric calculations and SQL statement generation without manual intervention. The system reads from predefined mapping tables and automatically generates accurate SQL statements, ensuring consistent and error-free computation while maintaining the ability to customize metrics through table configuration.
Solution Approach 2:
The patent replaces the mechanical process of manual editing with an automated computational system. Instead of human operators manually updating metrics and writing SQL statements, the system uses automated algorithms to read mapping tables and generate calculations, substituting human-prone processes with machine precision.
3Productivity
If automated generation of SQL statements is implemented, then time consumption and error rate are reduced, but system complexity increases
Solution Approach 1:
The system divides the complex QoE calculation process into distinct segments: KPI data collection, SKQI calculation (intermediate quality indicators), and final QoE computation. Each segment is handled by separate SQL statements generated from mapping tables, making the overall complex process manageable and maintainable through modular organization.
Solution Approach 2:
The patent introduces SKQI (sub-key quality indicators) as intermediary elements between raw KPIs and final QoE metrics. These intermediate indicators serve as mediators that simplify the calculation process by breaking down complex QoE computations into manageable steps, where mapping tables define the relationships between KPIs, SKQIs, and final QoE values.
4Productivity
If automated generation of SQL statements is implemented, then productivity is improved, but initial system setup complexity increases
Solution Approach 1:
The system automatically generates SQL statements and configuration files without requiring manual programming or complex setup procedures. The automated generation process reads from mapping tables and produces ready-to-execute SQL code, significantly improving productivity while reducing the burden of initial system implementation and maintenance.
Data Source
AI summary
The disclosed technology teaches automating evaluation of QoE for data communication services in a wireless network—accessing performance indicators, mappings for the performance indicators to SKQIs in a SKQI parameters table, and mappings for SKQIs to KQIs in an SKQI-to-KQI mapping table. The disclosed method includes configuring first and second functions that, respectively, roll up performance indicators into SKQIs based on parameters stored in the SKQI parameters table to calculate SKQI scores and weights, and roll up SKQIs into KQIs and QoE based on parameters stored in SKQI-to-KQI and SKQI-to-QoE mapping tables, to calculate KQI and QoE scores. Also disclosed is generating first SQL statements that invoke the first function to calculate the SKQIs and second SQL statements that invoke the second function to calculate KQIs and QoE, storing generated first and second SQL statements in non-volatile storage and calculating KQIs and QoE using the automatically generated SQL statements.