Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the
precise execution of the corresponding downstream processes (inbound and outbound …

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

Tensor programs often need to process large tensors (vectors, matrices, or higher order
tensors) that require a specialized storage format for their memory layout. Several such …

This paper introduces semi-ring dictionaries, a powerful class of compositional and purely
functional collections that subsume other collection types such as sets, multisets, arrays …

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 …

This paper overviews an approach that addresses machine learning over relational data as
a database problem. This is justified by two observations. First, the input to the learning task …

Python data science libraries such as Pandas and NumPy have recently gained immense
popularity. Although these libraries are feature-rich and easy to use, their scalability …

IFAQ is an intermediate representation and compilation framework for hybrid database and
machine learning workloads expressible using iterative programs with functional aggregate …

We introduce a framework for automatically choosing data structures for efficient query
processing. Our contributions are twofold. First, we introduce a novel low-level intermediate …

Data analytics over normalized databases typically requires computing and materializing
expensive joins (wide-tables). Factorized query execution models execution as message …