The training, maintenance, deployment, monitoring, organization and documentation of
machine learning (ML) models–in short model management–is a critical task in virtually all …

Providing machine learning (ML) over relational data is a mainstream requirement for data
analytics systems. While almost all the ML tools require the input data to be presented as a …

Machine learning (ML) pipelines for model training and validation typically include
preprocessing, such as data cleaning and feature engineering, prior to training an ML …

Analytics tasks manipulate structured data with variants of relational algebra (RA) and
quantitative data with variants of linear algebra (LA). The two computational models have …

Software systems that learn from data with machine learning (ML) are used in critical
decision-making processes. Unfortunately, real-world experience shows that the pipelines …

SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning
Page 1 © 2017 IBM Corporation SPOOF: Sum-Product Optimization and Operator Fusion for …

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

There is a long tradition in understanding graphs by investigating their adjacency matrices
by means of linear algebra. Similarly, logic-based graph query languages are commonly …

We investigate the expressive power of MATLANG, a formal language for matrix
manipulation based on common matrix operations and linear algebra. The language can be …

We present Samsara, a domain-specific language for declarative machine learning in
cluster environments. Samsara allows its users to specify programs using a set of common …