By Carlo Batini
Poor facts caliber can heavily prevent or harm the potency and effectiveness of corporations and companies. The growing to be information of such repercussions has resulted in significant public tasks just like the "Data caliber Act" within the united states and the "European 2003/98" directive of the ecu Parliament.
Batini and Scannapieco current a accomplished and systematic advent to the extensive set of concerns regarding facts caliber. they begin with an in depth description of other information caliber dimensions, like accuracy, completeness, and consistency, and their significance in numerous different types of info, like federated info, net information, or time-dependent info, and in numerous facts different types categorised in accordance with frequency of switch, like reliable, long term, and regularly altering information. The book's large description of ideas and methodologies from middle info caliber examine in addition to from comparable fields like information mining, likelihood thought, statistical information research, and computer studying provides an exceptional assessment of the present state-of-the-art. The presentation is finished via a quick description and demanding comparability of instruments and functional methodologies, in order to aid readers to solve their very own caliber problems.
This e-book is a perfect mix of the stability of theoretical foundations and the applicability of functional ways. it really is very best for everybody – researchers, scholars, or execs – drawn to a complete evaluate of information caliber concerns. moreover, it is going to function the foundation for an introductory path 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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Of course, this may not be the case, as different applications can weight the attributes of a tuple differently. The attribute completeness evaluates the percentage of specified values in the column corresponding to the attribute with respect to the total number of values that should have been specified. 4, let us consider an application calculating the average of the votes obtained by students. The absence of some values for the Vote attribute simply implies a deviation in the calculation of the average; therefore, a characterization of Vote completeness may be useful.
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. As an example, in [59], a trust model for information peers is proposed, in which a trust level is associated to a certain peer for each typology of data provided to the community. The interested reader can find more details on trust issues in peer-to-peer systems in Chapter 9. 6 Approaches to the Definition of Data Quality Dimensions In this section we focus on the general proposals for dimensions by illustrating some of them.
The interested reader can find further details in [159]. 3 Time-Related Dimensions: Currency, Timeliness, and Volatility An important aspect of data is their change and update in time. In Chapter 1 we provided a classification of types of data according to the temporal dimension, in terms of stable, long-term-changing, and frequently changing data. The principal time-related dimensions proposed for characterizing the above three types of data are currency, volatility, and timeliness. 3 Time-Related Dimensions: Currency, Timeliness, and Volatility 29 Currency concerns how promptly data are updated.