With the increasing demand for real-time analytics and decision making, anomaly detection
methods need to operate over streams of values and handle drifts in data distribution …

The recent popularity of learned embeddings has fueled the growth of massive collections of
high-dimensional (high-d) vectors that model complex data. Finding similar vectors in these …

We propose Hercules, a parallel tree-based technique for exact similarity search on massive
disk-based data series collections. We present novel index construction and query …

Data series indexes are necessary for managing and analyzing the increasing amounts of
data series collections that are nowadays available. These indexes support both exact and …

Data series indexes are necessary for managing and analyzing the increasing amounts of
data series collections that are nowadays available. These indexes support both exact and …

Vector data is prevalent across business and scientific applications, and its popularity is
growing with the proliferation of learned embeddings. Vector data collections often reach …

Similarity search is a core operation of many critical applications, involving massive
collections of high-dimensional (high-d) objects. Objects can be data series, text …

This paper presents Odyssey, a novel distributed data-series processing framework that
efficiently addresses the critical challenges of exhibiting good speedup and ensuring high …

The ever-growing collections of data series create a pressing need for efficient similarity
search, which serves as the backbone for various analytics pipelines. Recent studies have …

Epilepsy is a chronic neurological disease, ranked as the second most burdensome
neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is …