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

Research is very important in the pandemic situation of COVID-19 to deliver a speedy
solution to this problem. COVID-19 has presented governments, corporations and ordinary …

Joins are expensive, especially on large data and/or multiple relations. One promising
approach in mitigating their high costs is to just return a simple random sample of the full join …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

Machine learning algorithms are commonly specified in linear algebra (LA). LA expressions
can be rewritten into more efficient forms, by taking advantage of input properties such as …

Data lakes are becoming increasingly prevalent for Big Data management and data
analytics. In contrast to traditional 'schema-on-write'approaches such as data warehouses …

The area of Principles of Data Management (PDM) has made crucial contributions to the
development of formal frameworks for understanding and managing data and knowledge …

In the era of big data, computing exact answers to analytical queries becomes prohibitively
expensive. This greatly increases the value of approaches that can compute efficiently …

Machine learning (ML) applications become increasingly common in many domains. ML
systems to execute these workloads include numerical computing frameworks and libraries …

This paper overviews factorized databases and their application to machine learning. The
key observation underlying this work is that state-of-the-art relational query processing …