Multi-energy load forecasting forms the foundation of the operation and scheduling of
integrated energy systems. Nevertheless, insufficient data and underutilization of the …

Cardinality estimation is a fundamental and challenging problem in query optimization.
Recently, a number of learned models have been proposed for this task. Often, these …

While selecting the execution plan for a given query based on a single estimated cost is a
generally-adopted strategy, it is usually error-prone and fails to comprehensively profile the …

Approximate query processing (AQP) is one of the key techniques to cope with big data
querying problem on account that it obtains approximate answers efficiently. To address non …

Cardinality estimation is critical for database management systems (DBMSs) to execute
query optimization tasks, which can guide the query optimizer in choosing the best …

Learning-based models have shown promise in addressing query optimization challenges
in the database field, where the learned cost model plays a central role. While these models …

The query optimizer relies on accurate cardinality estimation to generate efficient execution
plans. Despite decades of research, existing cardinality estimation remain inaccurate for …

Learning representations for query plans play a pivotal role in machine learning-based
query optimizers of database management systems. To this end, particular model …