Naïve Bayesian Network for Real-Time Event Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Business Activity Monitoring (BAM) systems face challenges in accurately predicting future events and identifying root causes of issues in real-time, with existing learning algorithms being time- and resource-intensive and lacking real-time implementation.
Innovation Solution
The system processes key performance indicators (KPIs) using a time-series transform, matches them to predefined waveforms, and feeds them into a dynamically updatable Naïve Bayesian Network (NBN) for prediction, while also performing chi-square analysis for root cause identification, all operating substantially in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning algorithms are implemented for real-time BAM data processing, then prediction accuracy is improved, but computational time and resource consumption increase
Solution Approach 1:
The system segments the learning algorithm into two distinct phases: an offline training phase where the Naïve Bayesian Network is trained on historical BAM data to build prediction models, and an online inference phase where the trained model rapidly processes real-time data. This segmentation allows computationally intensive training to occur outside real-time constraints while maintaining high prediction accuracy during actual operation.
Solution Approach 2:
The system performs preliminary action by pre-training the Naïve Bayesian Network model on historical data before real-time processing begins. The model learns patterns, correlations, and prediction rules in advance, so that during real-time operation, only lightweight inference computations are needed, dramatically reducing computational time while preserving accuracy.
2Measurement precision
If comprehensive BAM data is analyzed for root cause identification, then diagnostic accuracy is improved, but processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features and data elements from comprehensive BAM data for root cause analysis. The Naïve Bayesian Network identifies and focuses on key performance indicators and event attributes that have the strongest correlation with target events, filtering out redundant information. This extraction maintains diagnostic accuracy by concentrating on critical factors while reducing processing complexity.
Solution Approach 2:
The system applies local quality by assigning different levels of analysis depth to different data elements based on their relevance. High-impact KPIs receive more detailed processing and modeling attention, while less critical data elements undergo simpler processing. This differentiated approach improves diagnostic accuracy for critical issues without uniformly increasing processing complexity across all data.
Data Source
AI summary
In certain example embodiments of this invention, there is provided systems and/or methods for processing BAM-related data to predict when events of interest are about to happen and/or to identify the root causes of, or at least data correlated with, such events of interest. In certain example embodiments, key performance indicators (KPIs) are gathered and gardened. The gardening process may identify KPI values of interest (e.g., based on a Z-factor analysis thereof across one or more collection intervals). The gardened KPIs may be processed using a time-series transform (e.g., a Fast Fourier Transform), matched to one of a plurality of predefined waveforms, and fed into a dynamic Naïve Bayesian Network (NBN) for prediction. The gardened data also may be used to determine the relevance of the KPI for root causes of problems (e.g., based on a chi-square analysis).