KPI Calculation Plan Generation for Data Sync
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Solution Overview
Problem
The calculation of multiple key performance indicators (KPIs) over millions of data points is complex and requires significant computational resources, especially for real-time monitoring, and involves challenges such as redundant steps and incomplete data handling.
Innovation Solution
A system generates and executes plans to collect, process, and output data, determining necessary operations based on KPI definitions and data sources, allowing for efficient merging and execution of operations, and detects new data to address incomplete periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple KPIs are calculated over millions of data points with real-time monitoring requirements, then measurement precision and reliability are improved, but computational resource consumption and system complexity increase significantly
Solution Approach 1:
The patent segments the KPI calculation process into distinct operational phases: data collection from multiple sources, data validation and enrichment, aggregation operations, and final KPI computation. Each phase is independently managed and can be optimized separately, reducing overall system complexity while maintaining calculation accuracy across millions of data points.
Solution Approach 2:
The system performs preliminary data validation, enrichment, and aggregation operations before the actual KPI calculation. Data is pre-processed and stored in optimized formats, and correlation relationships between data sources are pre-established. This preliminary action reduces the computational burden during real-time KPI calculation, maintaining precision without proportionally increasing system complexity.
2Measurement precision
If more KPIs and data sources are added to the monitoring system, then measurement precision and comprehensiveness are improved, but computational resource consumption and operation time increase
Solution Approach 1:
The patent merges multiple data collection operations that access the same data sources into single unified operations. When multiple KPIs require data from the same source, the system consolidates these requests into one data retrieval operation, performing aggregation and enrichment once for reuse. This significantly reduces calculation time while maintaining comprehensive monitoring coverage across numerous KPIs and data sources.
Solution Approach 2:
The system implements universal data aggregation operations that can serve multiple KPI calculation purposes simultaneously. A single aggregated dataset can be used by multiple KPI definitions, and the aggregation logic is designed to be reusable across different KPI types. This multi-functionality reduces redundant computations and maintains comprehensive monitoring without proportionally increasing calculation time.
3Measurement precision
If data aggregation and correlation operations are performed to ensure accurate KPI calculations, then measurement precision is improved, but computational resource consumption and networking resources are wasted on redundant steps
Solution Approach 1:
The system performs data aggregation, enrichment, and correlation operations in advance, before the actual KPI calculation is needed. Aggregated data is pre-computed and stored in optimized structures, and correlation relationships between data sources are pre-established. This preliminary action ensures measurement precision is maintained while avoiding redundant computational and networking resource consumption during real-time KPI calculations.
Solution Approach 2:
The patent merges redundant data access and aggregation operations into single unified operations. When multiple KPIs require data from the same sources with similar aggregation requirements, the system consolidates these operations, performing the data retrieval and aggregation once and sharing the results across multiple KPI calculations. This eliminates redundant networking and computational resource waste while maintaining accurate KPI precision.
4Reliability
If the system performs comprehensive data validation and handling of incomplete data periods, then reliability is improved, but operation time and computational resources increase
Solution Approach 1:
The system performs comprehensive data validation, completeness checks, and handling of incomplete data periods in advance of KPI calculation. Data quality issues are identified and corrected during the preliminary data collection and aggregation phase, rather than during the critical KPI computation phase. This ensures high reliability and data completeness while minimizing the time impact on actual KPI delivery.
Data Source
AI summary
A unit may use metadata associated with a key performance indicator (KPI) to generate a set of instructions for another unit on how it can load and process the data needed to calculate the value of that KPI, as well as how to calculate the KPI itself and how and where to persist the results. Using information about that set, the unit can generate a different set of instructions to detect any data-syncing issues that may have affected previously obtained KPI values. For that purpose, the unit comprises a plan execution module and a plan generator module adapted to generate plans for calculating KPIs. A plan comprises instructions to: i.) access to a processing system's raw data on data sources, ii.) process said information, iii.) and send the output to data sinks.