By Carlo Batini, Monica Scannapieco
This publication presents a scientific and comparative description of the tremendous variety of examine concerns with regards to the standard of information and data. It does so through supplying a legitimate, built-in and finished evaluation of the cutting-edge and destiny improvement of knowledge and knowledge caliber in databases and knowledge systems.
To this finish, it offers an intensive description of the options that represent the center of information and data caliber examine, together with checklist linkage (also referred to as item identification), facts integration, blunders localization and correction, and examines the similar recommendations in a entire and unique methodological framework. caliber size definitions and followed types also are analyzed intimately, and variations among the proposed strategies are highlighted and mentioned. moreover, whereas systematically describing facts and knowledge caliber as an independent examine zone, paradigms and affects deriving from different parts, similar to chance thought, statistical info research, facts mining, wisdom illustration, and desktop studying also are incorporated. final no longer least, the booklet additionally highlights very useful strategies, equivalent to methodologies, benchmarks for the best suggestions, case reviews, and examples.
The booklet has been written essentially for researchers within the fields of databases and data administration or in average sciences who're drawn to investigating houses of knowledge and data that experience an influence at the caliber of experiments, strategies and on actual existence. the fabric provided can be sufficiently self-contained for masters or PhD-level classes, and it covers all of the basics and themes with out the necessity for different textbooks. info and knowledge process directors and practitioners, who care for platforms uncovered to data-quality concerns and therefore want a systematization of the sector and useful tools within the quarter, also will enjoy the mix of concrete useful techniques with sound theoretical formalisms.
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Extra resources for Data and Information Quality: Dimensions, Principles and Techniques
Let us focus on Fig. 2, showing an image representing a flower. Instinctively, the image on the right-hand side is considered of better quality in comparison to the image on the left-hand side. At the same time, it is not immediate to identify the dimension(s) that we are considering to come to such a conclusion. In the preface, we provided a list of different information types. In the next section, we introduce several classifications of information relevant to quality issues, while issues related to information quality in several types of information systems will be considered in Sect.
A second point of view sees information as a product. This point of view is adopted, for example, in the IP-MAP model (see ), an extension of the Information Manufacturing Product model , which will be discussed in detail in Chap. 6; the IP-MAP model identifies a parallelism between the quality of information and the quality of products as managed by manufacturing companies. In this model, three different types of information are distinguished: • Raw data items are considered smaller data units.
Let us consider the relation Movies introduced in Chap. 1, shown in Fig. 1. The value Rman Holiday in movie 3 for Title is syntactically inaccurate, since it does not correspond to any title of a movie. Roman Holiday is the closest movie name to Rman Holiday; indeed, the edit distance between Rman Holiday and Roman Holiday is equal to 1 and simply corresponds to the insertion of the char o in the string Rman Holidays. Since 1 is the edit distance, the measure of syntactic accuracy is 1. More precisely, given a comparison function C, we may define a measure of syntactic accuracy of a value v with respect to a definition domain D, as the minimum value of C, when comparing v with all the values in D.