Data Lineage Analysis
An end-to-end framework for data lineage analysis covering link pattern recognition, fault diagnosis, and early warning
With the increasing complexity of data platforms, achieving real-time prediction and tracing of data link failures has become a critical issue that needs to be addressed. We propose an End-to-End Full-Link intelligent analysis framework (EEFL) based on data lineage. This framework combines graph structures with deep learning algorithms to achieve link pattern recognition and fault warning. First, a dynamic data lineage graph model is constructed and topological features are extracted using a graph neural network (GNN). Through temporal edge weight optimization and semi-supervised clustering, typical link patterns are automatically classified. Second, a hybrid fault diagnosis model is designed, using a temporal convolutional network (TCN) to capture long-term dependencies between link metrics and combining it with a GNN to analyze topological mutations. This model accurately classifies various fault types, including data outages, latency anomalies, and data contamination. Finally, a dynamic threshold warning mechanism is introduced, combining Bayesian optimization and online learning to adaptively adjust alarm triggering conditions and effectively reduce false alarm rates. We verify the generalization ability of the model using actual enterprise data and simulation data. Experimental results show that EEFL can achieve an average Acc of 92.73% across two datasets, which is significantly better than traditional methods and provides intelligent decision for data governance.
Authors: Rongxu Hou, Shaobo Zhang, Hongjiang Wang, Siwei Li & Yiying Zhang
Executive Impact: Key Performance Metrics
The End-to-End Full-Link intelligent analysis framework (EEFL) demonstrates superior performance across critical data lineage tasks, ensuring robust and reliable data operations.
0
Link Pattern Recognition Accuracy
0
Fault Diagnosis Accuracy
0
Early Warning System Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
EEFL End-to-End Framework
The proposed End-to-End Full-Link intelligent analysis framework (EEFL) integrates dynamic graph modeling, deep learning for pattern recognition and fault diagnosis, and an adaptive early warning system. Its modular design ensures comprehensive coverage from data ingestion to real-time alerting.
Enterprise Process Flow
Dynamic data lineage graph
→
Link identification
→
TCN fault diagnosis
→
Gaussian-Bayesian threshold optimization
Link Pattern Recognition Performance
The EEFL framework achieves state-of-the-art accuracy in recognizing complex link patterns within dynamic data lineage graphs, significantly surpassing traditional and advanced GNN models.
0
Accuracy in Link Pattern Recognition
| Algorithm | Accuracy | Precision | Recall | F₁ score |
|---|---|---|---|---|
| GAT | 93.10%±0.35% | 92.50%±0.39% | 92.00%±0.41% | 92.20%±0.33% |
| WGCN | 94.60%±0.29% | 94.00%±0.33% | 93.80%±0.35% | 93.90%±0.28% |
| TGN | 96.15%±0.25% | 95.80%±0.28% | 95.60%±0.31% | 95.70%±0.24% |
| EEFL | 97.20%±0.21% | 96.80%±0.23% | 96.50%±0.26% | 96.60%±0.19% |
Key Insight: EEFL's dynamic graph modeling and weighted GNN approach allows for a more accurate distinction of complex patterns like linear chains, star topologies, and cyclic dependencies, leading to its superior performance.
Fault Diagnosis Capabilities
EEFL's hybrid TCN-GNN model effectively captures both temporal and topological dependencies to accurately classify various fault types, including data outages, latency anomalies, and data contamination.
0
Accuracy in Fault Diagnosis
| Algorithm | Accuracy | Precision | Recall | F₁ score |
|---|---|---|---|---|
| GTNN | 92.30%±0.36% | 91.80%±0.40% | 91.50%±0.43% | 91.60%±0.34% |
| GSTNN | 93.50%±0.31% | 93.00%±0.35% | 92.80%±0.38% | 92.90%±0.30% |
| MTGNN | 94.75%±0.28% | 94.40%±0.32% | 94.20%±0.34% | 94.30%±0.26% |
| EEFL | 95.80%±0.22% | 95.50%±0.25% | 95.20%±0.28% | 95.30%±0.20% |
Key Insight: The fusion of TCN and GNN in EEFL's diagnostic model addresses the limitations of single time series or graph models, providing a comprehensive understanding of complex fault scenarios.
Dynamic Threshold Early Warning
The Bayesian optimization and FTRL-based dynamic threshold mechanism significantly reduces false alarms and missed alerts by adaptively adjusting to real-time data distribution changes, improving system reliability.
0
Accuracy of Early Warning System
| Algorithm | Accuracy | FAR | MAR | ALT (min) |
|---|---|---|---|---|
| ST | 84.20% | 16.5% | 17.0% | 1.2 |
| EWMT | 90.20% | 10.3% | 10.6% | 4.6 |
| OmniAnomaly | 92.40% | 8.1% | 8.3% | 5.8 |
| EEFL | 93.80% | 6.6% | 6.9% | 8.2 |
Key Insight: EEFL's adaptive threshold adjustment mechanism, leveraging Bayesian optimization and FTRL, excels in dynamic environments where data distribution shifts, outperforming static and simpler adaptive methods.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing an advanced data lineage analysis framework.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A structured approach to integrating EEFL into your data operations, from initial setup to advanced fault prediction.
Phase 01: Dynamic Graph Modeling & Pattern Recognition
Establish the dynamic data lineage graph, leveraging GNNs for automatic extraction and classification of complex link patterns to enhance data visibility and understanding.
Phase 02: Hybrid Fault Diagnosis Model Development
Implement the TCN-GNN hybrid model to capture temporal dependencies and topological mutations, enabling accurate classification of various fault types like data outages and latency anomalies.
Phase 03: Adaptive Early Warning System Deployment
Integrate the dynamic threshold warning mechanism using Bayesian optimization and FTRL online learning to adaptively adjust alerts, significantly reducing false positives and missed reports.
Phase 04: Continuous Optimization & Scalability
Monitor performance, gather feedback, and continuously refine the EEFL framework. Explore scalability in heterogeneous data environments and prepare for real-time operational integration.
Ready to Transform Your Data Operations?
Unlock the full potential of your data with an intelligent, end-to-end lineage analysis solution. Our experts are ready to guide you.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat