This paper investigates ultrafilters in the context of connectivity systems, defined as pairs
$(X, f) $ where $ X $ is a finite set and $ f $ is a symmetric submodular function. Ultrafilters …

Worst-case optimal join algorithms are attractive from a theoretical point of view, as they offer
asymptotically better runtime than binary joins on certain types of queries. In particular, they …

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 …

We consider the task of lexicographic direct access to query answers. That is, we want to
simulate an array containing the answers of a join query sorted in a lexicographic order …

Recent data search platforms use ML task-based utility measures rather than metadata-
based keywords, to search large dataset corpora. Requesters submit a training dataset and …

We study the fine-grained complexity of evaluating Boolean Conjunctive Queries and their
generalization to sum-of-product problems over an arbitrary semiring. For these problems …

This paper overviews an approach that addresses machine learning over relational data as
a database problem. This is justified by two observations. First, the input to the learning task …

Dashboards are vital in modern business intelligence tools, providing non-technical users
with an interface to access comprehensive business data. With the rise of cloud technology …

Top-k queries have been studied intensively in the database community and they are an
important means to reduce query cost when only the" best" or" most interesting" results are …

AutoML services provide a way for non-expert users to benefit from high-quality ML models
without worrying about model design and deployment, in exchange for a charge per hour …