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