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 …

There is an increasingly pressing need, by several applications in diverse domains, for
developing techniques able to index and mine very large collections of sequences, or data …

Data series similarity search is a core operation for several data series analysis applications
across many domains. This has attracted lots of interest that led to the development of …

Advances in air monitoring methods have made it possible to analyze large-scale air
pollution phenomena. Mining potential air pollution information from large-scale air pollution …

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 …

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

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 …

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