Data series are a special type of multidimensional data present in numerous domains,
where similarity search is a key operation that has been extensively studied in the data …

Data series similarity search is an important operation and at the core of several analysis
tasks and applications related to data series collections. Despite the fact that data series …

Increasingly large data series collections are becoming commonplace across many different
domains and applications. A key operation in the analysis of data series collections is …

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 …

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 …

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

Similarity-based search over time-series databases has been a hot research topic for a long
history, which is widely used in many applications, including multimedia retrieval, data …

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 …