State-of-the-art neural language models can now be used to solve ad-hoc language tasks
through zero-shot prompting without the need for supervised training. This approach has …

In the context of data visualization and analytics, this report outlines some of the challenges
and emerging applications that arise in the Big Data era. In particularly, fourteen …

Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

Explainable artificial intelligence has mainly focused on static learning scenarios so far. We
are interested in dynamic scenarios where data is sampled progressively, and learning is …

Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing
multidimensional data. However, when data is a live streaming feed, conventional DR …

In recent years the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm has
become one of the most used and insightful techniques for exploratory data analysis of high …

Time-series data-usually presented in the form of lines-plays an important role in many
domains such as finance, meteorology, health, and urban informatics. Yet, little has been …

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

Natural language is a promising alternative interface to DBMSs because it enables non-
technical users to formulate complex questions in a more concise manner than SQL …

The machine learning (ML) life cycle involves a series of iterative steps, from the effective
gathering and preparation of the data—including complex feature engineering processes …