Vectorized Incident Matching for Faster Cloud Remediation
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Solution Overview
Problem
Incident detection in big data systems is challenging due to the complexity and scale of data and system components, leading to delays in responding to incidents, which can result in downtime, compromised data integrity, and security breaches.
Innovation Solution
A system that generates indexed and vectorized incident records using a vector database to detect similar incidents, allowing for rapid identification and implementation of remediation actions based on previous effective responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional incident detection methods are used, then system complexity is managed, but response time increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing incident records into standardized formats and storing them in a vector database with embeddings. This preparation allows the system to quickly match new incidents with historical ones without performing complex analysis during the critical response window, thereby improving response speed while managing complexity through advance preparation.
Solution Approach 2:
The system creates a simplified representation (copy) of complex incident data by converting incident records into vector embeddings that capture essential characteristics. This copying process transforms complex, unstructured incident data into a compact format that enables fast similarity searching while preserving the necessary information for effective incident response.
2Loss of time
If manual incident analysis is performed, then accuracy is maintained, but time consumption increases
Solution Approach 1:
The system replaces manual mechanical analysis with an automated machine learning-based similarity matching system. By using vector embeddings and similarity search algorithms, the system automatically identifies relevant historical incidents without human intervention, significantly reducing analysis time while maintaining accuracy through the use of trained models that understand incident patterns.
Solution Approach 2:
The system performs self-service by automatically analyzing new incidents against the historical database and generating remediation recommendations without requiring manual review. The incident response system autonomously matches incidents, retrieves relevant historical data, and suggests appropriate actions, eliminating the need for time-consuming manual analysis while maintaining high accuracy through automated pattern recognition.
3Loss of information
If comprehensive incident data is stored, then information availability improves, but data volume increases making processing slower
Solution Approach 1:
The system extracts only the essential information from comprehensive incident data by converting it into condensed vector representations. This extraction process retains the most important incident characteristics needed for matching and response while discarding redundant details, thereby reducing data volume for processing while maintaining information availability for effective incident response.
Solution Approach 2:
The system changes the representation parameters of incident data by transforming detailed incident records into vector embeddings that capture essential patterns in a compact format. This parameter transformation allows the system to store and process incident information efficiently, improving data processing speed while preserving the necessary information through the semantic content of the vectors.
Data Source
AI summary
A system and method for detecting similar incident records for initiating remediation actions in a computing environment is presented. The method includes: accessing a plurality of incident records, each incident record generated based on extracted values from a plurality of event records; generating, for each incident record of the plurality of incident records, an indexed incident record based on a plurality of predetermined categorical attributes; generating a vectorized indexed incident record for each of the incident records; storing the vectorized indexed incident record for each of the incident records in a vector database; vectorizing a new incident record; querying the vector database based on the vectorized new incident record to detect a similar vector; and generating a similar incident record based on the detected similar vector.