Download e-book for kindle: Data Quality: Concepts, Methodologies and Techniques by Carlo Batini

By Carlo Batini

ISBN-10: 3540331727

ISBN-13: 9783540331728

ISBN-10: 3540331735

ISBN-13: 9783540331735

Poor information caliber can heavily prevent or harm the potency and effectiveness of enterprises and companies. The starting to be understanding of such repercussions has resulted in significant public projects just like the "Data caliber Act" within the united states and the "European 2003/98" directive of the eu Parliament.

Batini and Scannapieco current a finished and systematic creation to the extensive set of matters on the topic of info caliber. they begin with an in depth description of other facts caliber dimensions, like accuracy, completeness, and consistency, and their significance in numerous different types of info, like federated facts, net info, or time-dependent information, and in numerous information different types categorized based on frequency of swap, like reliable, long term, and regularly altering info. The book's broad description of recommendations and methodologies from center facts caliber study in addition to from comparable fields like facts mining, likelihood conception, statistical facts research, and desktop studying supplies a very good review of the present state-of-the-art. The presentation is done by way of a quick description and significant comparability of instruments and useful methodologies, in an effort to aid readers to unravel their very own caliber problems.

This e-book is a perfect mix of the stability of theoretical foundations and the applicability of sensible ways. it truly is preferrred for everybody – researchers, scholars, or execs – drawn to a finished review of information caliber concerns. moreover, it is going to function the foundation for an introductory direction or for self-study in this topic.

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Extra resources for Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)

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These dimensions are believability, reputation, and objectivity. Believability considers whether a certain source provides data that can be regarded as true, real and credible. Reputation considers how trustable is the information source. Objectivity takes into account impartiality of sources in data provisioning. , in Wand and Wang [199]). More recently, with the increasing interest in peer-to-peer systems, the quality characterization of a source (or peer) is gaining importance. Indeed, in such systems that are completely open, there is the need to assess and filter data that circulate in the system, and one possibility is to rely on the trustability of each peer.

This is a radical change from old, centralized systems (still widespread in some organizations, such as banks), where data sources and data flows are accurately controlled and monitored. In this context, new quality dimensions arise; among them we now discuss interpretability, synchronization in time series, and, in more detail, accessibility and (the set of) dimensions proposed for characterizing the quality of an information source. Other dimensions are introduced and discussed in [50]. Interpretability concerns the documentation and metadata that are available to correctly interpret the meaning and properties of data sources.

Nevertheless, it is also known that more courses will be added to the list, pending their approval. Therefore, there is the need to apprehend how the list will evolve in time with respect to completeness. The traditional completeness dimension provides only a static characterization of completeness. In order to consider the temporal dynamics of completeness, as needed in Web information systems, we introduce the notion of completability. We consider a function C(t), defined as the value of completeness at the instant t, with t ∈ [t pub, t max], where t pub is the initial instant of publication of data and t max corresponds to the maximum time within which the series of the different scheduled updates will be completed.

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Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications) by Carlo Batini


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