Python has become the prime language for application development in the data science and
machine learning domains. However, data scientists are not necessarily experienced …

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

The diversity and complexity of modern data management applications have led to the
extension of the relational paradigm with syntactic and semantic support for User-Defined …

Integrated data analysis (IDA) pipelines---that combine data management (DM) and query
processing, high-performance computing (HPC), and machine learning (ML) training and …

Compiler optimization plays an increasingly important role to boost the performance of
machine learning models for data processing and management. With increasingly complex …

The simplicity of Python and its rich set of libraries has made it the most popular language
for data science. Moreover, the interpreted nature of Python offers an easy debugging …

Modern data management applications involve complex processing tasks over large
volumes of data. Although this falls naturally within the scope of relational databases, many …

Predicate pushdown is a widely adopted query optimization. Existing systems and prior work
mostly use pattern-matching rules to decide when a predicate can be pushed through …

User-defined functions (UDFs) have long been used as the de facto way to extend the
capabilities of data management systems. However, they are restricted to the specificities of …

Although user-defined functions (UDFs) are a popular way to augment SQL's declarative
approach with procedural code, the mismatch between programming paradigms creates a …