Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

As massive data acquisition and storage becomes increasingly affordable, a wide variety of
enterprises are employing statisticians to engage in sophisticated data analysis. In this …

Modern advanced analytics applications make use of machine learning techniques and
contain multiple steps of domain-specific and general-purpose processing with high …

Many modern enterprises are collecting data at the most detailed level possible, creating
data repositories ranging from terabytes to petabytes in size. The ability to apply …

ABSTRACT Pandas–the Python Data Analysis Library–is a powerful and widely used
framework for data analytics. In this work we present our approach to push down the …

SystemML aims at declarative, large-scale machine learning (ML) on top of MapReduce,
where high-level ML scripts with R-like syntax are compiled to programs of MR jobs. The …

We present Cumulon, a system designed to help users rapidly develop and intelligently
deploy matrix-based big-data analysis programs in the cloud. Cumulon features a flexible …

Declarative large-scale machine learning (ML) aims at flexible specification of ML algorithms
and automatic generation of hybrid runtime plans ranging from single node, in-memory …

Dataframes have become universally popular as a means to represent data in various
stages of structure, and manipulate it using a rich set of operators---thereby becoming an …

Accelerating machine learning (ML) over relational data is a key focus of the database
community. While many real-world datasets are multi-table, most ML tools expect single …