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 …

Recording sensor data is seldom a perfect process. Failures in power, communication or
storage can leave occasional blocks of data missing, affecting not only real-time monitoring …

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 …

Subsequence anomaly (or outlier) detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches that have been …

In the last 15 years, data series motif and discord discovery have emerged as two useful and
well-used primitives for data series mining, with applications to many domains, including …

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 …

The analysis of time-series data associated with modernday industrial operations and
scientific experiments is now pushing both computational power and resources to their …

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 different domains. However, the state-of-the-art techniques fail to deliver the …