In the last two decades, interactive visualization and analysis have become a central tool in
data-driven decision making. Concurrently to the contributions in data visualization …

Data visualization is by far the most commonly used mechanism to explore data, especially
by novice data analysts and data scientists. And yet, current visual analytics tools are rather …

Advances in deep learning have led to a resurgence of interest in video analytics. In an
exploratory video analytics pipeline, a data scientist often starts by searching for a global …

We observe significant overlaps in the computations performed by user jobs in modern
shared analytics clusters. Naïvely computing the same subexpressions multiple times results …

This paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-
memory optimization and execution engine for batches of aggregates over the input …

Analytics-as-a-service, or analytics job service, is emerging as a new paradigm for data
analytics, be it in a cloud environment or within enterprises. In this setting, users are not …

Many large-scale machine learning (ML) systems allow specifying custom ML algorithms by
means of linear algebra programs, and then automatically generate efficient execution …

Modern hardware heterogeneity brings efficiency and performance opportunities for
analytical query processing. In the presence of continuous data volume and complexity …

Machine learning (ML) computations commonly execute on expensive specialized
hardware, such as GPUs and TPUs, which provide high FLOPs and performance-per-watt …

The advent of columnar data analytics engines fueled a series of optimizations on the scan
operator. New designs include column-group storage, vectorized execution, shared scans …