Comparable dependencies over heterogeneous data

Abstract

To study the data dependencies over heterogeneous data in dataspaces, we define a general dependency form, namely comparable dependencies (cds), which specifies constraints on comparable attributes. It covers the semantics of a broad class of dependencies in databases, including functional dependencies (fds), metric functional dependencies (mfds), and matching dependencies (mds). As we illustrated, comparable dependencies are useful in real practice of dataspaces, such as semantic query optimization. Due to heterogeneous data in dataspaces, the first question, known as the validation problem, is to tell whether a dependency (almost) holds in a data instance. Unfortunately, as we proved, the validation problem with certain error or confidence guarantee is generally hard. In fact, the confidence validation problem is also np-hard to approximate to within any constant factor. Nevertheless, we develop several approaches for efficient approximation computation, such as greedy and randomized approaches with an approximation bound on the

Learning Individual Models for Imputation

“…Future studies may further consider to answer queries directly over multiple imputation candidates suggested by different individual models [21], rather determining exactly one imputation. Moreover, instead of study the regression models directly on data values, one may further investigate the dependency models [33] on the distances of values [37] for imputation.…”

Characterizing functional dependencies in formal concept analysis with pattern structures

“…with the heuristic driven approach realized by the algorithm FastFDs [43]. Finally, to deal with the issues of very large heterogeneous databases and uncertainty, approximate approaches (greedy and randomized approaches with approximation bounds on errors) are also developed [38].…”

Characterization of Database Dependencies with FCA and Pattern Structures

“…fuzzy FDs [8], matching dependencies [29], constraint generating dependencies [7]. Moreover, it is still an active topic of research in the database community [9,10,29,30].…”

“…FD Mine [35] takes the approach of reducing the number of candidate dependencies by using the axioms that apply to them. Approximate Dependencies computation is explained in [15], and other different kind of FD-alike dependencies can be found in [29] and [30].…”