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 requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Finding line-chart images similar to a given line-chart image query is a common task in data
exploration and image query systems, eg finding similar trends in stock markets or medical …

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 …

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 …

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 …

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

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