We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set
convolutional network, tailored to representing relational query plans, that employs set …

Exhaustive enumeration of all possible join orders is often avoided, and most optimizers
leverage heuristics to prune the search space. The design and implementation of heuristics …

The immense popularity of SQLite shows that there is a need for unobtrusive in-process data
management solutions. However, there is no such system yet geared towards analytical …

Query optimizer is at the heart of the database systems. Cost-based optimizer studied in this
paper is adopted in almost all current database systems. A cost-based optimizer introduces …

We study the problem of optimizing subgraph queries using the new worst-case optimal join
plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time …

Database Management Systems (DBMS) are used ubiquitously. To efficiently access data,
they apply sophisticated optimizations. Incorrect optimizations can result in logic bugs, which …

The Social Network Benchmark's Business Intelligence workload (SNB BI) is a
comprehensive graph OLAP benchmark targeting analytical data systems capable of …

In this work we introduce a novel approach to the problem of cardinality estimation over
multijoin queries. Our approach leveraging randomized hashing and data sketching to …

TPC-H continues to be the most widely used benchmark for relational OLAP systems. It
poses a number of challenges, also known as" choke points", which database systems have …

The prospect of achieving computational speedups by exploiting quantum phenomena
makes the use of quantum processing units (QPUs) attractive for many algorithmic database …