Modern data analytics workloads combine relational data processing with machine learning
(ML). Most DBMS handle these workloads by offloading these ML operations to external …

Modern location-based applications rely extensively on the efficient processing of spatial
data and queries. Spatial query engines are commonly engineered as an extension to a …

The enormous quantity of data produced every day together with advances in data analytics
has led to a proliferation of data management and analysis systems. Typically, these …

Optimizing machine learning (ML) workloads on structured data is a key concern for data
platforms. One class of optimizations called" factorized ML" helps reduce ML runtimes over …

The major goals of this project were to devise programming models and supporting tools
that simplify the construction of specialized high‐performance generators, leading to …

In recent years, DRAM has become a critical bottleneck for scaling modern data-intensive
applications, such as analytical databases. The CPU-memory gap still exists, while queries …

Modern location-based applications rely extensively on the efficient processing of spatial
data and queries. Spatial query engines are commonly engineered as an extension to a …

The availability of modern hardware with large amounts of memory created a shift in the
development of data-centric software; from optimizing I/O operations to optimizing …

Although current augmented, virtual, and mixed reality (AR/VR/MR) systems are facing
advanced and immersive experience in the entertainment industry with countless media …