Learned cardinality estimation (CE) has recently gained significant attention for replacing
long-studied traditional CE with machine learning, especially for deep learning. However …

Machine learning models based on neural networks (NNs) are enjoying ever-increasing
attention in the Database (DB) community, both in research and practice. However, an …

We present a dynamic index structure for join sampling. Built for an (equi-) join Q---let IN be
the total number of tuples in the input relations of Q---the structure uses~ O (IN) space …

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 …

Although dominant for tabular data, ML libraries that train tree models over normalized
databases (eg, LightGBM, XGBoost) require the data to be denormalized as a single table …

> Sampling is one of the most widely employed approximations in big data processing.
Among various challenges in sampling design, sampling for join is particularly intriguing yet …

Joining records with all other records that meet a linkage condition can result in an
astronomically large number of combinations due to many-to-many relationships. For such …

In the interactive data exploration, approximate query processing (AQP) can be used to
quickly return query results at the cost of accuracy. For online AQP, the sampler can be …

Probabilistic programming languages (PPLs) are essential for reasoning under uncertainty.
Even though many real-world probabilistic programs involve discrete distributions, the state …

For interactive data exploration, approximate query processing (AQP) is a useful approach
that usually uses samples to provide a timely response for queries by trading query …