Query driven cardinality estimation models learn from a historical log of queries. They are
lightweight, having low storage requirements, fast inference and training, and are easily …

Knob tuning plays an important role in database optimization, which tunes knob settings to
optimize the database performance or improve resource utilization. However, there are …

Cardinality estimation is one of the most fundamental and challenging problems in query
optimization. Neither classical nor learning-based methods yield satisfactory performance …

The emergence of ML in various cloud system management tasks (eg, workload autoscaling
and job scheduling) has become a core driver of ML-centric cloud platforms. However, there …

Query plan is widely used as input in machine learning for databases (ML4DB) research,
with query plan representation as a critical step. However, existing studies typically focus on …

This paper introduces ZeroTune, a novel cost model for parallel and distributed stream
processing that can be used to effectively set initial parallelism degrees of streaming …

Machine Learning (ML) is changing DBs as many DB components are being replaced by ML
models. One open problem in this setting is how to update such ML models in the presence …

Machine learning models are routinely integrated into process mining pipelines to carry out
tasks like data transformation, noise reduction, anomaly detection, classification, and …

This paper addresses the challenge of predicting the level of parallelism in distributed
stream processing (DSP) systems, which are essential to deal with different high workload …

Data encoding has been applied to database systems for decades as it mitigates bandwidth
bottlenecks and reduces storage requirements. But even in the presence of these …