  In data systems, activities or events are continuously collected in the field
to trace their proper executions. Logging, which means recording sequences of
events, can be used for analyzing system failures and malfunctions, and
identifying the causes and locations of such issues. In our research we focus
on creating an Anomaly detection models for system logs. The task of anomaly
detection is identifying unexpected events in dataset, which differ from the
normal behavior. Anomaly detection models also assist in data systems analysis
tasks.
  Modern systems may produce such a large amount of events monitoring every
individual event is not feasible. In such cases, the events are often
aggregated over a fixed period of time, reporting the number of times every
event has occurred in that time period. This aggregation facilitates scaling,
but requires a different approach for anomaly detection. In this research, we
present a thorough analysis of the aggregated data and the relationships
between aggregated events. Based on the initial phase of our research we
present graphs representations of our aggregated dataset, which represent the
different relationships between aggregated instances in the same context.
  Using the graph representation, we propose Multiple-graphs autoencoder MGAE,
a novel convolutional graphs-autoencoder model which exploits the relationships
of the aggregated instances in our unique dataset. MGAE outperforms standard
graph-autoencoder models and the different experiments. With our novel MGAE we
present 60% decrease in reconstruction error in comparison to standard graph
autoencoder, which is expressed in reconstructing high-degree relationships.
