Replication Package: Domain-Unspecific Anomaly Detection for Runtime Monitoring Data - Types of Anomalies, Detection Approaches, Dataset Parameters, and Hypotheses

This replication package includes general remarks on anomaly detection approaches identified via an extension of a literature study (Soldani, J., & Brogi, A. (2022). Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. ACM Computing Surveys (CSUR), 55(3), 1-39.) and  15 interview participants from various domains to address the methodology and findings for domain unspecific parameters extracted from runtime monitoring data to detect anomalies. 

Due to confidentiality, we are not allowed to provide the video recordings or transcripts. 

The folder contains:

• RQ1_Definition_and_Understandings.md: summarises the interview participants' statements regarding the definition of an anomaly and industry examples
• RQ2_Anomaly_Detection_Approaches.md: summarises the interview participants' statements regarding rule-based and AI-based and pros/cons thereof
• RQ3_RQ4_Parameters_RuntimeMonitoringData.xlxs: summarises the interview participants' statements & anomaly detection tools (including industry-relevant & benchmark datasets) regarding parameters suitable for detecting anomalies