Estimating the selectivity of a query is a key step in almost any cost-based query optimizer.
Most of today's databases rely on histograms or samples that are periodically refreshed by …

This article provides an overview of recent developments in mainmemory database systems.
With growing memory sizes and memory prices dropping by a factor of 10 every 5 years …

Today's query optimizers use fast selectivity estimation techniques but are known to be
susceptible to large estimation errors. Recent work on supervised learned models for …

Estimating selectivities remains a critical task in query processing. Optimizers rely on the
accuracy of selectivities when generating execution plans and, in approximate query …

Background. For some years now, Machine Learning (ML) has been applied to the
cardinality estimation problem [8, 12, 32, 33]. In general, ML means arbitrary function …

BACKGROUND Query optimization depends in large part on efficient and accurate
cardinality estimates of intermediate results and the plan for the order of a sequence or set of …

We introduce a new concept of leveraging traditional data statistics as dynamic data integrity
constraints. These data statistics produce transient database constraints, which are valid as …

(57) ABSTRACT A system for cardinality estimation for database query planning is provided.
In some implementations, the system performs operations comprising selecting a subset of …

In columnar databases, data is generally stored in an encoded format to save storage space
and reduce I/O. Popular encoding schemes include dictionary encoding, delta encoding, run …

Every year more and more advanced approaches to cardinality estimation are published,
using learned models or other data and workload specific synopses. In contrast, the majority …