14 citations · 44 across the 16 of their papers we have counts for
7 papers · 1 filter
A Taxonomy of Anomalies in Log Data
Thorsten Wittkopp, Philipp Wiesner, Dominik Scheinert +1
Log data anomaly detection is a core component in the area of artificial intelligence for IT operations. However, the large amount of existing methods makes it hard to choose the r…
LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision
Thorsten Wittkopp, Philipp Wiesner, Dominik Scheinert +1
With increasing scale and complexity of cloud operations, automated detection of anomalies in monitoring data such as logs will be an essential part of managing future IT infrastru…
On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds
Dominik Scheinert, Alireza Alamgiralem, Jonathan Bader +3
With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important. Since managing a dedicated infrastructure is i…
A2Log: Attentive Augmented Log Anomaly Detection
Thorsten Wittkopp, Alexander Acker, Sasho Nedelkoski +4
Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. As log lines record events during the execution of IT services, they are a…
Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts
Dominik Scheinert, Lauritz Thamsen, Houkun Zhu +4
Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforw…
Decentralized Federated Learning Preserves Model and Data Privacy
Thorsten Wittkopp, Alexander Acker
The increasing complexity of IT systems requires solutions, that support operations in case of failure. Therefore, Artificial Intelligence for System Operations (AIOps) is a field…