The sports industry utilizes science to improve short to long-term team and player
management regarding budget, health, tactics, training, and most importantly performance …

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 classification is an important and challenging problem in data science.
Explaining the classification decisions by finding the discriminant parts of the input that led …

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 …

With the advent of the intelligent era, more and more artificial intelligence algorithms are
widely used and a large number of user data are collected in the cloud server for sharing …

A key operation for the (increasingly large) data series collection analysis is similarity
search. According to recent studies, SAX-based indexes offer state-of-the-art performance …

Data series similarity search is a core operation for several data series analysis applications
across many different domains. However, the state-of-the-art techniques fail to deliver the …

Similarity search is a core operation of many critical data science applications, involving
massive collections of high-dimensional objects. Similarity search finds objects in a …

Existing systems dealing with the increasing volume of data series cannot guarantee
interactive response times, even for fundamental tasks such as similarity search. Therefore …