000 05250nam a22005055i 4500
001 978-3-031-01892-3
003 DE-He213
005 20240730164116.0
007 cr nn 008mamaa
008 220601s2012 sz | s |||| 0|eng d
020 _a9783031018923
_9978-3-031-01892-3
024 7 _a10.1007/978-3-031-01892-3
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aFan, Wenfei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982168
245 1 0 _aFoundations of Data Quality Management
_h[electronic resource] /
_cby Wenfei Fan, Floris Geerts.
250 _a1st ed. 2012.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2012.
300 _aXV, 201 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Management,
_x2153-5426
505 0 _aData Quality: An Overview -- Conditional Dependencies -- Cleaning Data with Conditional Dependencies -- Data Deduplication -- Information Completeness -- Data Currency -- Interactions between Data Quality Issues.
520 _aData quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues.
650 0 _aComputer networks .
_931572
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aComputer Communication Networks.
_982169
650 2 4 _aData Structures and Information Theory.
_931923
700 1 _aGeerts, Floris.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982170
710 2 _aSpringerLink (Online service)
_982171
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007644
776 0 8 _iPrinted edition:
_z9783031030208
830 0 _aSynthesis Lectures on Data Management,
_x2153-5426
_982172
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01892-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c85308
_d85308