Recent work on" learned indexes" has changed the way we look at the decades-old field of
DBMS indexing. The key idea is that indexes can be thought of as" models" that predict the …

Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in
data processing and analytics where many algorithm designs have incorporated heuristics …

Query optimizers rely on accurate cardinality estimates to produce good execution plans.
Despite decades of research, existing cardinality estimators are inaccurate for complex …

Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …

Filtering data based on predicates is one of the most fundamental operations for any modern
data warehouse. Techniques to accelerate the execution of filter expressions include …

Indexes can improve query-processing performance by avoiding full table scans. Although
traditional indexes (eg, B+-tree) have been widely used, learned indexes are proposed to …

Index plays an essential role in modern database engines to accelerate the query
processing. The new paradigm of" learned index" has significantly changed the way of …

Learned indexes have been proposed to replace classic index structures like B-Tree with
machine learning (ML) models. They require to replace both the indexes and query …

Machine learning, especially deep learning, is used increasingly to enable better solutions
for data management tasks previously solved by other means, including database indexing …

The increasing scale and complexity of workloads in modern cloud services highlight a
crucial challenge in automated index tuning: recommending high-quality indexes while …