Modern database management systems (DBMS) expose hundreds of configurable knobs to
control system behaviours. Determining the appropriate values for these knobs to improve …

Business Intelligence (BI) is crucial in modern enterprises and billion-dollar business.
Traditionally, technical experts like database administrators would manually prepare BI …

Table Question Answering (TQA) presents a substantial challenge at the intersection of
natural language processing and data analytics. This task involves answering natural …

Large Language Models (LLMs) have shown useful applications in a variety of tasks,
including data wrangling. In this paper, we investigate the use of an off-the-shelf LLM for …

Schema matching constitutes a pivotal phase in the data ingestion process for contemporary
database systems. Its objective is to discern pairwise similarities between two sets of …

The scarcity of training data restricts a neural network from capturing schema diversity and
intricacies, hindering schema-matching models' generalization capabilities. In this paper, we …

Judging the equivalence between two SQL queries is a fundamental problem with many
practical applications in data management and SQL generation (ie, evaluating the quality of …

We introduce SMUTF, a unique approach for large-scale tabular data schema matching
(SM), which assumes that supervised learning does not affect performance in open-domain …

The article presents an approach to the automatic derivation of conceptual database models
from heterogeneous source artifacts. The approach is based on the integration of conceptual …

Schema matching is a crucial task in data integration, involving the alignment of a source
database schema with a target schema to establish correspondence between their …