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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Conditional Differential Dependencies (CDDs)
verfasst von : Selasi Kwashie, Jixue Liu, Jiuyong Li, Feiyue Ye
Erschienen in: Advances in Databases and Information Systems
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Abstract
Differential dependency (DD) is a newly proposed data dependency theory that captures the relationships amongst data values. Like the classical functional dependency (FD) theory, DDs are defined to hold over entire instances of relations. This paper proposes a novel extension of the DD theory to hold over subsets of relations, called conditional DD (CDD), similar to the relaxations of FD to conditional FD (CFD) [4] and conditional FD with predicates (CFDPs) [6]. In this work, we present: the formal definitions; the consistency and implication analysis; and a set of axioms to infer CDDs. Furthermore, we study the discovery problem of CDDs and present an algorithm for mining a minimal cover set \(\varSigma _c\) of constant CDDs from a given instance of a relation. And, we propose an interestingness measure for ranking discovered CDDs and reducing the size \(|\varSigma _c|\) of \(\varSigma _c\). We demonstrate the efficiency, effectiveness and scalability of the discovery algorithm through experiments on both real and synthetic datasets.