is to understand the dataset at hand and its metadata. The process of metadata discovery is
known as data profiling. Profiling activities range from ad-hoc approaches, such as eye …

Effective query optimization is a core feature of any database management system. While
most query optimization techniques make use of simple metadata, such as cardinalities and …

Denial constraints (DCs) are a generalization of many other integrity constraints (ICs) widely
used in databases, such as key constraints, functional dependencies, or order …

Besides the conventional schema-oriented tasks, data dependencies are recently revisited
for data quality applications, such as violation detection, data repairing and record matching …

Patterns (or regex-based expressions) are widely used to constrain the format of a domain
(or a column), eg, a Year column should contain only four digits, and thus a value like “1980 …

We analyze the problem of discovering dependencies from distributed big data. Existing
(non-distributed) algorithms focus on minimizing computation by pruning the search space …

Intelligent devices often produce time series data that suffer from significant data quality
issues. While the utilization of data dependency in error detection and data repair has been …

We investigate the problem of discovering approximate denial constraints (DCs), for finding
DCs that hold with some exceptions to avoid overfitting real-life dirty data and facilitate data …

Integrity constraints (ICs) are useful for expressing and enforcing application semantics.
Formulating ICs manually, however, requires domain expertise, is prone to human error, and …

Pointwise order dependencies (PODs) are dependencies that specify ordering semantics on
attributes of tuples. POD discovery refers to the process of identifying the set Σ of valid and …