Data Dependencies Extended for Variety and Veracity: A Family Tree

Abstract

Besides the conventional schema-oriented tasks, data dependencies are recently revisited for data quality applications, such as violation detection, data repairing and record matching. To address the variety and veracity issues of big data, data dependencies have been extended as data quality rules to adapt to various data types, ranging from (1) categorical data with equality relationships to (2) heterogeneous data with similarity relationships, and (3) numerical data with order relationships. In this survey, we briefly review the recent proposals on data dependencies categorized into the aforesaid types of data. In addition to (a) the concepts of these data dependency notations, we investigate (b) the extension relationships between data dependencies, e.g., conditional functional dependencies (CFDs) extend the conventional functional dependencies (FDs). It forms a family tree of extensions, mostly rooted in FDs, helping us understand the expressive power of various data dependencies. Moreover, we summarize (c) the discovery of dependencies from data, since data dependencies are often unlikely to be manually specified in a traditional way, given the huge volume and high variety of big data. We further outline (d) the applications of the extended data dependencies, in particular in data quality practice. It guides users to select proper data dependencies with sufficient expressive power and reasonable discovery cost. Finally, we conclude with several directions of future studies on the emerging data.