Incident Similarity Detection Using Vectorized Record Indexing
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Solution Overview
Problem
In big data environments, detecting and responding to incidents is challenging due to the complexity and scale of the systems, leading to delays in addressing issues that can cause downtime, compromised data integrity, and security breaches.
Innovation Solution
A system utilizing a vector database and a large language model (LLM) to index, vectorize, and analyze incident records, enabling the detection of similar incidents and providing remediation strategies based on similarity scores and textual explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional incident detection tools are used in big data environments, then incident detection capability is maintained, but response time is delayed due to the sheer volume and velocity of data
Solution Approach 1:
The incident detection system is segmented into multiple specialized components: vectorization module for converting incident attributes to vectors, similarity computation module for calculating distances between incident vectors, and incident record generation module for creating structured records. This segmentation allows each component to handle specific tasks efficiently, reducing overall response time despite system complexity
Solution Approach 2:
A vector database serves as an intermediary between raw incident data and analysis operations. Incident records are converted to vector representations and stored in the vector database, enabling efficient similarity searches without repeatedly processing raw data. This intermediary structure accelerates incident response by providing quick access to comparable incident patterns
2Reliability
If the volume of incident records increases to cover more system components, then detection coverage is improved, but the time to analyze and prioritize incidents increases
Solution Approach 1:
The system transforms incident records from structured data into vector representations, changing the parameter space from discrete attributes to continuous vector space. This parameter transformation enables the use of distance-based similarity metrics, allowing rapid comparison across large volumes of incident records while maintaining comprehensive detection coverage
Solution Approach 2:
Instead of analyzing raw incident records directly, the system creates vector copies of incident attributes and stores them in the vector database. These vector copies preserve the essential characteristics of incidents while enabling efficient similarity computations, thus maintaining detection coverage while improving analysis speed
3Productivity
If manual incident response processes are used, then response accuracy is maintained, but response time increases leading to increased downtime
Solution Approach 1:
The system replaces manual incident analysis with automated vector-based similarity computation. The vectorization process and distance calculations are performed automatically by the system, eliminating manual review while maintaining detection accuracy through mathematical similarity metrics that objectively compare incident patterns
4Measurement precision
If comprehensive incident attributes are collected to improve incident understanding, then detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts essential attributes from comprehensive incident data and represents them as vectors in a reduced-dimensional space. This extraction process retains the most discriminative features needed for accurate detection while eliminating redundant information, thus maintaining detection accuracy while reducing processing complexity
Data Source
AI summary
A system and method for detecting similar incident records for initiating remediation actions in a computing environment is provided. The method includes: receiving a plurality of incident records, each incident record generated based on extracted values from a plurality of event records; generating an indexed incident record based on a plurality of predetermined categorical attributes; vectorizing a string of categorical attributes to generate a vectorized indexed incident record; storing the vectorized indexed incident record 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.