Many organizations are shifting to a data management paradigm called the" Lakehouse,"
which implements the functionality of structured data warehouses on top of unstructured …

Graph-based intermediate representations (IRs) are widely used for powerful compiler
optimizations, either interprocedurally in pure functional languages, or intraprocedurally in …

TPC-H continues to be the most widely used benchmark for relational OLAP systems. It
poses a number of challenges, also known as" choke points", which database systems have …

Today's users of data processing systems come from different domains, have different levels
of expertise, and prefer different programming languages. As a result, analytical workload …

User-defined functions (UDFs) have been widely used to overcome the expressivity
limitations of SQL and complement its declarative nature with functional capabilities. UDFs …

Stream Processing Engines (SPEs) execute long-running queries on unbounded data
streams. They follow an interpretation-based processing model and do not perform runtime …

Datalog has gained prominence in program analysis due to its expressiveness and ease of
use. Its generic fixpoint resolution algorithm over relational domains simplifies the …

Today's data science pipelines often rely on user-defined functions (UDFs) written in Python.
But interpreted Python code is slow, and Python UDFs cannot be compiled to machine code …

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

Training of deep learning models depends on gradient descent and end-to-end
differentiation. Under the slogan of differentiable programming, there is an increasing …