Given a relation instance r, and an error threshold ε, the approx- imate DC discovery problem is to find all ε-approximate minimal DCs that hold on r. The discovery of exact DCs is a special case of this problem, where the error threshold is set to zero.
The algorithm combines data structures called position list indexes with techniques based on predicate selectivity to efficiently validate DC candidates.
We present denial constraint finder (DCFINDER), a novel al- gorithm to efficiently discover both approximate and exact DCs. DCFINDER iterates over the data ...
People also ask
What are denial constraints?
What is the theory of exact constraints?
Jan 8, 2020 · Denial constraints (DCs) are an integrity constraint formalism widely used to detect inconsistencies in data. Several algorithms have been ...
Approximation allows us to discover more accurate constraints in inconsistent databases and detect rules that are generally correct but may have a few ex- ...
We investigate the problem of discovering approximate denial con- straints (DCs), for finding DCs that hold with some exceptions to avoid overfitting real-life ...
To handle datasets that may have data errors, we extend. FASTDC to discover approximate constraints. Finally, we further extend it to discover DCs involving ...
To handle datasets that may have data errors, we extend. FASTDC to discover approximate constraints. Finally, we further extend it to discover DCs involving ...
This paper lays out theoretical and practical foundations for DCs, including a set of sound inference rules and a linear algorithm for implication testing, ...
Abstract—Denial constraints (DCs) are data dependencies with high expressive power, offering great flexibility for modeling data quality rules.