Anomaly detection is a fundamental task for time-series analytics with important implications
for the downstream performance of many applications. Despite increasing academic interest …

Datasets of labeled network traces are essential for a multitude of machine learning (ML)
tasks in networking, yet their availability is hindered by privacy and maintenance concerns …

Recently, much attention has been devoted to the development of generative network traces
and their potential use in supplementing real-world data for a variety of data-driven …

Similarity search is a core analytical task, and its performance critically depends on the
choice of distance measure. For time-series querying, elastic measures achieve state-of-the …

Clustering is one of the most popular time-series tasks because it enables unsupervised
data exploration and often serves as a subroutine or preprocessing step for other tasks …

As the proportion of elderly individuals in developed countries continues to rise globally,
addressing their healthcare needs, particularly in preserving their autonomy, is of paramount …

Distance measures are core building blocks in time-series analysis and the subject of active
research for decades. Unfortunately, the most detailed experimental study in this area is …

Cloud gaming, wherein game graphics is rendered in the cloud and streamed back to the
user as real-time video, expands the gaming market to billions of users who do not have …

Human Activity Recognition (HAR) is a challenging, multi-label classification problem as
activities may co-occur and sensor signals corresponding to the same activity may vary in …

Network traffic analysis increasingly uses complex machine learning models as the internet
consolidates and traffic gets more encrypted. However, over high-bandwidth networks, flows …