Incident Data Pipeline Correlation for Faster Root Cause Analysis

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Solution Overview

Problem

Existing IT systems face inefficiencies in processing complex data and identifying the root causes of incidents, leading to significant burdens on IT teams and potential system outages, which can impact a company's reputation and resources.

Innovation Solution

A data pipeline system that aggregates, processes, and analyzes incident data through a series of interconnected components, including a collection point, front gate processor, data storage, processing platform, and data sink layers, utilizing machine learning algorithms for real-time processing and correlation analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If a data pipeline system is implemented to process incident data through multiple components (collection point, front gate processor, data storage, processing platform, data sink layers), then the system's ability to identify incident patterns and reduce resolution time is improved, but the device complexity increases

Engineering Contradiction:

Improveincident resolution timeVSAvoiddata pipeline system complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The data pipeline system is divided into multiple specialized components: collection point for data ingestion, front gate processor for initial filtering, data storage for persistent storage, processing platform for real-time analysis, and data sink layers for output. Each component handles specific tasks independently, enabling parallel processing and reducing overall incident resolution time while maintaining manageable complexity through clear separation of concerns

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The processing platform acts as an intermediary between the front gate processor and data sink layers, performing real-time data processing and correlation analysis. This intermediary component transforms raw incident data into actionable insights, enabling rapid identification of incident patterns without requiring direct complex interactions between all system components

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If machine learning algorithms are used for real-time processing and correlation analysis of incident data, then the measurement precision of incident root cause identification is improved, but the use of energy and computational resources increases

Engineering Contradiction:

Improveroot cause identification accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The front gate processor performs preliminary filtering and aggregation of incident data before it reaches the machine learning processing platform. By pre-processing data to extract only relevant features and remove noise, the system reduces the computational burden on machine learning algorithms, enabling high-precision root cause identification with lower energy consumption

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Different processing techniques are applied to different portions of incident data based on their characteristics. Machine learning algorithms are applied selectively to correlated incident patterns rather than all raw data, while simpler rule-based processing handles routine incidents. This localized application of processing power optimizes both accuracy and resource efficiency

Inventive Principle:

Principle #3Local quality

3Productivity

If the system processes and correlates large amounts of incident data from multiple data sources, then the productivity of IT operations is improved, but the quantity of data to be processed increases

Engineering Contradiction:

ImproveIT operations efficiencyVSAvoidvolume of incident data

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system extracts only the most relevant features and attributes from large volumes of incident data at the front gate processor level. By identifying and extracting key diagnostic information while discarding redundant data, the system maintains high IT operations productivity through efficient correlation analysis without being overwhelmed by the total data volume

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The data pipeline processes all incident data through the collection point and front gate processor, but applies full machine learning correlation analysis only to subsets of data that meet specific criteria (e.g., incidents with multiple correlated symptoms). This partial application of intensive processing maintains productivity for critical incidents while reducing overall computational load

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12405805B2Systems and methods for processing incident data through a data pipeline

Publication Date: 2025.09.02 FIDELITY INFORMATION SERVICES LLC

AI summary

A computer implemented method for processing data through a data pipeline is disclosed. The method includes: receiving, by a collection point, data from one or more data sources, the collection point being configured to at least one of extract, transform, or load the data; transferring the data from the collection point to a front gate processor, the front gate processor being configured to process the data; transferring the processed data from the front gate processor to a data storage system, the data storage system being configured to store the processed data; transferring the processed data from the front gate processor to a processing platform; and transferring the processed data from the processing platform to one or more data sink layers, each of the one or more data sink layers being configured to provide short term storage of the processed data.