## 1 Introduction
What truly matters here are the around 5% of traces that carry anomalies: errors, exceptions, instances of high latency, or other forms of soft errors. However, the ideal scenario would involve analyzing the entire set of traces, identifying anomalies, and retaining them for thorough examination. In this paper, we advocate for a multi-purpose and hierarchical strategy combining head-based and tail-based scenarios, focusing on the efficiency of application troubleshooting and root cause analysis (RCA). We are considering a multi-layered approach to efficiently capture and preserve different types of information within a system. Overall, this hierarchical sampling approach efficiently manages trace data by prioritizing information based on its relevance and importance for system analysis and troubleshooting. It balances the need to reduce data volume with the requirement to retain critical details for comprehensive understanding and problem resolution. One of the main benefits of DT is improved application performance, resulting in reduced mean time to detect (MTTD) and mean time to repair (MTTR) IT issues. DT enables penetration to the bottom of application performance issues faster, often before users notice anything wrong. Upon discovering an issue, tracing can rapidly identify the root cause and address it. It also provides early warning capabilities when microservices are in poor health and highlights performance bottlenecks anywhere. However, this task is only feasible for a few system administrators or site reliability engineers (SREs) due to the large scale of modern distributed environments. The solution is an artificial intelligence (AI) for IT Operations (AIOps) strategy that leverages AI-powered software intelligence to automate developments, service deliveries, and troubleshooting. AIOps is a real game-changer for managing complex IT systems. In AIOps, where machine learning (ML) algorithms analyze vast amounts of data to automate IT operations and decision-making processes, transparency and interpretability are paramount. Trust in AIOps hinges on implementing explainable AI (XAI) solutions [19]. Those techniques enable stakeholders to understand and interpret the decisions made by AI models, build confidence in their reliability, and facilitate human-AI collaboration. By providing insights into how AI algorithms arrive at their conclusions, XAI fosters trust among users, reduces the risk of biased or erroneous outcomes, and enhances the adoption and effectiveness of AIOps solutions. We focus on rule-learning systems and show their efficiency in combination with trace sampling. Rule induction systems, such as RIPPER (Repeated Incremental Pruning to Produce Error Reduction) [20] and C5.0 rules [21] are common examples of XAI solutions. The paper is organized as follows. Section 2 describes the main trends of trace sampling and rule-induction. Section 3 describes the main idea of our approach. Section 4 discusses the main motivation and contribution of this research. Section 5 explains the methods of trace sampling based on properties when the goal is to preserve all available types, latencies, and errors uniformly. Section 6 presents an application troubleshooting strategy and the impact of noise reduction on a rule induction process. Section 7 summarizes the results and discusses future opportunities. Section 8 provides the list of patents related to this research.