LEADER 06462nam 22006135 450 001 9910254942103321 005 20200629184033.0 010 $a3-658-12225-0 024 7 $a10.1007/978-3-658-12225-6 035 $a(CKB)3710000000532726 035 $a(EBL)4189495 035 $a(SSID)ssj0001636523 035 $a(PQKBManifestationID)16387711 035 $a(PQKBTitleCode)TC0001636523 035 $a(PQKBWorkID)14950926 035 $a(PQKB)10902735 035 $a(DE-He213)978-3-658-12225-6 035 $a(MiAaPQ)EBC4189495 035 $a(PPN)224086758 035 $a(EXLCZ)993710000000532726 100 $a20151211d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData Quality Management with Semantic Technologies$b[electronic resource] /$fby Christian Fürber 205 $a1st ed. 2016. 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Gabler,$d2016. 215 $a1 online resource (230 p.) 300 $a"Research"--Cover. 311 $a3-658-12224-2 320 $aIncludes bibliographical references. 327 $aForeword; Preface; Table of Content; List of Figures; List of Tables; List of Abbreviations; PART I - Introduction, Economic Relevance, and Research Design ; 1 Introduction; 1.1 Initial Problem Statement; 1.2 Economic Relevance; 1.3 Organization of this Thesis; 1.4 Published Work; 1.4.1 Book Chapters; 1.4.2 Papers in Conference Proceedings; 1.4.3 Other Publications; 2 Research Design; 2.1 Semantic Technologies and Ontologies; 2.2 Research Goal; 2.3 Research Questions; 2.4 Research Methodology; 2.4.1 Design Science Research Methodology; 2.4.2 Ontology Development Methodology 327 $aPART II - Foundations: Data Quality, Semantic Technologies, and the Semantic Web 3 Data Quality; 3.1 Data Quality Dimensions; 3.2 Quality Influencing Artifacts; 3.3 Data Quality Problem Types; 3.3.1 Quality Problems of Attribute Values; 3.3.2 Multi-Attribute Quality Problems; 3.3.3 Problems of Object Instances; 3.3.4 Quality Problems of Data Models; 3.3.5 Common Linguistic Problems; 3.4 Data Quality in the Data Lifecycle; 3.4.1 Data Acquisition Phase; 3.4.2 Data Usage Phase; 3.4.3 Data Retirement Phase; 3.4.4 Data Quality Management throughout the Data Lifecycle 327 $a3.5 Data Quality Management Activities3.5.1 Total Information Quality Management (TIQM); 3.5.2 Total Data Quality Management (TDQM); 3.5.3 Comparison of Methodologies; 3.6 Role of Data Requirements in DQM; 3.6.1 Generic Data Requirement Types; 3.6.2 Challenges Related to Requirements Satisfaction; 4 Semantic Technologies; 4.1 Characteristics of an Ontology; 4.2 Knowledge Representation in the Semantic Web; 4.2.1 Resources and Uniform Resource Identifiers (URIs); 4.2.2 Core RDF Syntax: Triples, Literal Triples, and RDF Links; 4.2.3 Constructing an Ontology with RDF, RDFS, and OWL 327 $a4.2.4 Language Profiles of OWL and OWL 24.3 SPARQL Query Language for RDF; 4.4 Reasoning and Inferencing; 4.5 Ontologies and Relational Databases; 5 Data Quality in the Semantic Web; 5.1 Data Sources of the Semantic Web; 5.2 Semantic Web-specific Quality Problems; 5.2.1 Document Content Problems; 5.2.2 Data Format Problems; 5.2.3 Problems of Data Definitions and Semantics; 5.2.4 Problems of Data Classification; 5.2.5 Problems of Hyperlinks; 5.3 Distinct Characteristics of Data Quality in the Semantic Web; PART III - Development and Evaluation of the Semantic Data Quality Management Framework 327 $a6 Specification of Initial Requirements6.1 Motivating Scenario; 6.2 Initial Requirements for SDQM; 6.2.1 Task Requirements; 6.2.2 Functional Requirements; 6.2.3 Conditional Requirements; 6.2.4 Research Requirements; 6.3 Summary of SDQM's Requirements ; 7 Architecture of the Semantic Data Quality Management Framework (SDQM); 7.1 Data Acquisition Layer; 7.1.1 Reusable Artifacts for the Data Acquisition Layer; 7.1.2 Data Acquisition for SDQM; 7.2 Data Storage Layer; 7.2.1 Reusable Artifacts for Data Storage in SDQM; 7.2.2 The Data Storage Layer of SDQM; 7.3 Data Quality Management Vocabulary 327 $a7.3.1 Reuse of Existing Ontologies 330 $aChristian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis. The SDQM framework consists of three components that are evaluated in two different use cases. Moreover, this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work. Contents Data Quality and Semantic Technology Basics Data Quality in the Semantic Web Architecture and Evaluation of the Semantic Data Quality Management Framework Target Groups Researchers and students in the fields of economics, information systems and computer science Practitioners in the areas of data management, process management and business intelligence The Author Dr. Christian Fürber completed his doctoral study under the supervision of Prof. Dr. Martin Hepp at the E-Business and Web Science Research Group of the Universität der Bundeswehr München. He is founder and CEO of the Information Quality Institute GmbH, a company that consults organizations of any size to improve the quality of their data. 606 $aManagement information systems 606 $aKnowledge management 606 $aBusiness Information Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/522030 606 $aKnowledge Management$3https://scigraph.springernature.com/ontologies/product-market-codes/515030 615 0$aManagement information systems. 615 0$aKnowledge management. 615 14$aBusiness Information Systems. 615 24$aKnowledge Management. 676 $a650 700 $aFürber$b Christian$4aut$4http://id.loc.gov/vocabulary/relators/aut$0931713 906 $aBOOK 912 $a9910254942103321 996 $aData Quality Management with Semantic Technologies$92095751 997 $aUNINA