Percentile-Based Alert Generation for Event Data
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Solution Overview
Problem
Current database systems face challenges in efficiently generating and maintaining alert rules for event data, leading to time-consuming manual processes and potential errors due to outdated alerts.
Innovation Solution
Implementing a method that identifies Nth-percentile baseline results from historical event data to determine alert conditions, allowing for automated alert generation based on percentile comparisons between historical and real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule bases are constructed and maintained to trigger alerts, then alert functionality is provided, but time consumption and error rates increase significantly
Solution Approach 1:
The system automatically generates alert rules by analyzing historical event data and determining statistical baselines (e.g., Nth-percentile baselines) without requiring manual rule construction. The system self-maintains these rules by continuously updating baselines based on new data, eliminating the need for manual updates and reducing both time consumption and human errors in rule management.
Solution Approach 2:
The system transforms the approach from static manually-defined thresholds to dynamic statistical parameters derived from historical data. By calculating baselines such as Nth-percentile values from historical event counts and using these as automatic thresholds, the system adapts to changing patterns in event data while eliminating manual parameter tuning.
2Adaptability or versatility
If numerous manual rules are established to cover all event scenarios, then comprehensive alert coverage is achieved, but system complexity and difficulty in maintenance increase
Solution Approach 1:
The system implements a universal alert generation mechanism that automatically creates rules for any event type by analyzing historical data patterns. Instead of requiring separate manual rules for each event scenario, the system uses a single automated process that adapts to different event types, time periods, and patterns, providing comprehensive coverage without increasing rule base complexity.
Solution Approach 2:
The system automatically generates and maintains alert rules for all event scenarios by analyzing historical data, eliminating the need for manual rule construction for each scenario. The self-service approach continuously adapts to new event patterns and automatically updates rules, maintaining comprehensive coverage while keeping the system simple to operate.
3Ease of operation
If static alert thresholds are used, then simple alert logic is maintained, but outdated alerts fail to respond to changing event patterns
Solution Approach 1:
The system replaces static thresholds with dynamic baselines calculated from historical event data. The Nth-percentile baseline automatically adapts to changing event patterns by continuously incorporating new data, ensuring alerts remain timely and relevant. The dynamic nature of the baseline allows the system to respond to evolving event characteristics while maintaining simple alert logic based on comparative analysis.
Solution Approach 2:
The system pre-calculates statistical baselines from historical data before they are needed for alerting. By establishing the Nth-percentile baseline in advance based on historical patterns, the system prepares adaptive thresholds that will automatically respond to future event patterns, combining the simplicity of pre-established rules with the adaptability of data-driven thresholds.
Data Source
AI summary
Disclosed herein are systems and methods for providing percentile-based alerts from historical event-occurrence data. An embodiment of a method includes identifying a baseline result of an aggregation operation performed with respect to a detection set of attributes of event records. Each baseline result corresponds to a baseline set of one or more event records from a baseline time period within a baseline time window. The method includes determining a subject result of the aggregation operation performed with respect to the detection set of attributes of a subject set of the event records corresponding to a subject time period. The method includes determining, based on the baseline result and the subject result, whether the subject result indicates an alert condition for the detection set for the subject time period, and if so then outputting one or more alerts.