Efficient join processing is one of the most fundamental and well-studied tasks in database
research. In this work, we examine algorithms for natural join queries over many relations …

This work reviews how database theory uses tractable circuit classes from knowledge
compilation. We present relevant query evaluation tasks, and notions of tractable circuits …

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 …

Differential privacy has garnered significant attention in recent years due to its potential in
offering robust privacy protection for individual data during analysis. With the increasing …

Modern computing tasks such as real-time analytics require refresh of query results under
high update rates. Incremental View Maintenance (IVM) approaches this problem by …

In this paper, we describe a secure version of the classical Yannakakis algorithm for
computing free-connex join-aggregate queries. This protocol can be used in the secure two …

A general-purpose query engine that supports a large class of SQLs under differential
privacy is the holy grail in privacy-preserving query release. The join operator presents a …

Although dominant for tabular data, ML libraries that train tree models over normalized
databases (eg, LightGBM, XGBoost) require the data to be denormalized as a single table …

This paper studies how to use fast matrix multiplication to speed up query processing. As
observed, computing a two-table join and then projecting away the join attribute is …

Pipelines combining SQL-style business intelligence (BI) queries and linear algebra (LA)
are becoming increasingly common in industry. As a result, there is a growing need to unify …