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Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder
Tomer Meirman,
Roni Stern
,
Gilad Katz
Department of Software and Information Systems Engineering
Research output
:
Working paper/Preprint
›
Preprint
36
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Dive into the research topics of 'Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder'. Together they form a unique fingerprint.
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Keyphrases
Anomaly Detection
100%
Multigraph
100%
Graph Autoencoder
100%
System Failure
40%
Graph Representation
40%
Data Systems
40%
Standard Graphs
40%
Anomaly Detection Model
40%
Autoencoder Model
40%
System Analysis
20%
Event Sequences
20%
Modern Systems
20%
System Log
20%
Event Monitoring
20%
Fixed Period
20%
Analyzing System
20%
Unexpected Events
20%
Reconstruction Error
20%
Normal Behavior
20%
Data Activity
20%
Convolutional
20%
Timed Report
20%
Aggregated Datasets
20%
System Events
20%
Multiple Graphs
20%
Computer Science
Anomaly Detection
100%
Autoencoder
100%
Aggregated Data
100%
Graph Representation
40%
System Analysis
20%
Reconstruction Error
20%
Normal Behavior
20%
Unexpected Event
20%
Monitoring Event
20%
System Activity
20%