SLA Metric Computation via Materialized Views
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Solution Overview
Problem
In IT-enabled frameworks, monitoring Service Level Agreements (SLAs) is hindered by the computational overhead caused by large volumes of diverse data, leading to increased processing time and reduced system performance due to inefficient query execution.
Innovation Solution
A system and method that optimize metric computation by transforming data into a user-defined structure, creating a materialized view, and generating queries to execute on this view, reducing the need to query the entire dataset and thereby minimizing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If queries are executed on the entire large-volume database to monitor service levels, then complete and accurate performance tracking is achieved, but processing time increases and computational overhead increases
Solution Approach 1:
The patent segments the large-volume database into multiple partitions based on service level categories, provider types, and time periods. This segmentation allows queries to be executed on relevant subsets of data rather than the entire database, reducing processing time while maintaining complete and accurate performance tracking for each service level category.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing aggregated service level metrics in summary tables before queries are executed. This preliminary computation of service level performance data eliminates the need to process raw transactional data during monitoring queries, significantly reducing processing time while preserving measurement precision.
2Measurement precision
If queries are executed on the entire large-volume database to monitor service levels, then complete and accurate performance tracking is achieved, but computational overhead increases
Solution Approach 1:
The patent segments the database into structured partitions with predefined schemas optimized for specific query types. This segmentation reduces computational overhead by enabling the system to access only relevant data partitions and apply appropriate query optimization strategies for each segment, rather than processing the entire database uniformly.
Solution Approach 2:
The patent creates simplified copies of the database data in aggregated summary tables that contain pre-computed service level metrics. These copies allow monitoring queries to execute on reduced data structures with fewer computational operations, thereby reducing computational overhead while maintaining the precision needed for accurate performance tracking.
3Adaptability or versatility
If the system processes large volumes of diverse data formats, then comprehensive service level monitoring is achieved, but processing speed decreases
Solution Approach 1:
The patent transforms diverse data formats into a standardized internal representation with consistent schemas for service level data. This parameter change from diverse formats to standardized structures enables efficient processing by eliminating format conversion overhead during queries, while the transformation process preserves all necessary information for comprehensive service level monitoring.
Solution Approach 2:
The patent performs preliminary data transformation and standardization during the data ingestion phase, converting diverse formats into the standardized schema before storage. This preliminary action ensures that subsequent monitoring queries operate on uniformly structured data, significantly improving processing speed while maintaining comprehensive monitoring capability across all original data formats.
Data Source
AI summary
A system and method for optimizing the computation of metrics facilitating monitoring of service level agreements (SLA) in an IT-enabled framework to reduce computational overhead. A receiving module receives data associated with activities performed by one or more service providers. A transforming module transforms the data into a user-defined data structure using a transforming technique. A selecting module selects an attribute and parameters associated with a key performance indicator (KPI) of the one or more service providers. A view creating module creates a materialized view of the user-defined data structure based on the selected attribute. A query generating module m generates a query to be executed on the materialized view. A metric executing module executes the query on the materialized view in order to compute the metric.