Detecting anomalies in time series data is important in a variety of fields, including system
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …

Performance anomaly alerting based on trace data plays an important role in assuring the
quality of online service systems. However, engineers find that many anomalies reported by …

Dynamic time war** (DTW) is a well-known algorithm for time series elastic dissimilarity
measure. Its ability to deal with non-linear time distortions makes it helpful in a variety of data …

A key performance indicator (KPI) consists of critical time series data that reflect the runtime
states of network systems (eg, response time and available bandwidth). Despite the …

Data quality plays a vital role in scientific research and decision-making across industries.
Thus, it is crucial to incorporate the data quality control (DQC) process, which comprises …

Within today's large-scale systems, one anomaly can impact millions of users. Detecting
such events in real-time is essential to maintain the quality of services. It allows the …

Time series anomaly detection is an important research topic in the field of intelligent
operation and maintenance. When software systems are frequently updated with continuous …

In this research. we study anomaly detection in univariate time series and optimize
according to a business objective using a novel active learning approach. The motivation is …

Privacy concerns are amongst the core issues that will constrain the adoption of distributed
anomaly detection. Indeed, when outsourcing anomaly detection, ie with a party other than …

Diagnosing causes of performance variations in High-Performance Computing (HPC)
systems is a daunting chal-lenge due to the systems' scale and complexity. Variations in …