Anomaly detection is a fundamental data analytics task across scientific fields and
industries. In recent years, an increasing interest has been shown in the application of …

Recent advances in data collection technology, accompanied by the ever-rising volume and
velocity of streaming data, underscore the vital need for time series analytics. In this regard …

With the Internet of Things (IoT), a vast number of connected devices generate significant
data, necessitating efficient compression techniques to manage storage costs and enhance …

Deep learning (DL) is gaining popularity across many domains thanks to tools such as
TensorFlow and easier access to GPUs. But building large-scale DL applications is still too …

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 …

Time-series anomaly detection is a fundamental task across scientific fields and industries.
However, the field has long faced the``elephant in the room:''critical issues including flawed …

Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in
edge networks with sliced computing resources has mainly been studied for end devices …

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 …

Time series, as one of the most fundamental representations of sequential data, has been
extensively studied across diverse disciplines, including computer science, biology …

Distance measures have been recognized as one of the fundamental building blocks in time-
series analysis tasks, eg, querying, indexing, classification, clustering, anomaly detection …