The typical approach for learned DBMS components is to capture the behavior by running a
representative set of queries and use the observations to train a machine learning model …

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

Cardinality estimation (CardEst) plays a significant role in generating high-quality query
plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced …

Cardinality estimation is a fundamental but long unresolved problem in query optimization.
Recently, multiple papers from different research groups consistently report that learned …

In this paper, we reflect on the state of the art of Approximate Query Processing. Although
much technical progress has been made in this area of research, we are yet to see its impact …

In the last two decades, interactive visualization and analysis have become a central tool in
data-driven decision making. Concurrently to the contributions in data visualization …

Online analytical processing (OLAP) is a core functionality in database systems. The
performance of OLAP is crucial to make online decisions in many applications. However, it is …

Despite 25 years of research in academia, approximate query processing (AQP) has had
little industrial adoption. One of the major causes of this slow adoption is the reluctance of …

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 …

Previous approaches to learned cardinality estimation have focused on improving average
estimation error, but not all estimates matter equally. Since learned models inevitably make …