The traditional and well-established cost-based query optimizer approach enumerates
different execution plans for each query, assesses each plan with costs, and selects the plan …

Accurately estimating the cardinality (ie, the number of answers) of complex queries plays a
central role in database systems. This problem is particularly difficult in graph databases …

Data compression has recently experienced a revival in the domain of in-memory column
stores. In this field, a large corpus of lightweight integer compression algorithms plays a …

As available data increases, so too does the demand to dataset discovery. Existing studies
often yield coarse-grained results where significant information overlaps and non-relevant …

In recent times, learned query optimizer has becoming a hot research topic in learned
databases. It serves as the most suitable experimental plots for utilizing numerous machine …

Query cost estimation is a classical task for database management. Recently, researchers
have applied AI-driven methods to implement query cost estimation for achieving high …

In this paper, given a user's query set and budget, we aim to use the limited budget to help
users assemble a set of datasets that can enrich a base dataset by introducing the maximum …

Abstract (EN) In a world with an ever-increasing amount of data processed, providing tools
for highquality and fast data processing is imperative. Database Management Systems …

In database systems, accurate cardinality estimation is a cornerstone of effective query
optimization. In this context, estimators that use machine learning have shown significant …

Learning-based cardinality estimation methods outperform traditional methods by effectively
overcoming strong assumptions (attribute value independence and uniform distribution) …