The optimization of queries speeds up query processing in databases. One of the most time-
consuming tasks in query processing is the join operation, where the order of the joins plays …

This paper is an experimental and analytical study of two classes of summary-based
cardinality estimators that use statistics about input relations and small-size joins in the …

Big data processing at the production scale presents a highly complex environment for
resource optimization (RO), a problem crucial for meeting performance goals and budgetary …

Database systems are no longer used only for the storage of plain structured data and basic
analyses. An increasing role is also played by the integration of ML models, eg, neural …

Quantum computing is an emerging technology and has yet to be exploited by industries to
implement practical applications. Research has already laid the foundation for figuring out …

As Spark becomes a common big data analytics platform, its growing complexity makes
automatic tuning of numerous parameters critical for performance. Our work on Spark …

Cardinality estimation is a fundamental task in database query processing and optimization.
As shown in recent papers, machine learning (ML)-based approaches may deliver more …

Data preprocessing, the step of transforming data into a suitable format for training a model,
rarely happens within database systems but rather in external Python libraries and thus …

We study two classes of summary-based cardinality estimators that use statistics about input
relations and small-size joins:(i) optimistic estimators, which were defined in the context of …

Big data query processing has become increasingly important, prompting the development
and cloud deployment of numerous systems. However, automatically tuning the numerous …