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Biblioteca

MARC Lista (tabellare)
Current practices in workplace and organizational learning : revisiting the classics and advancing knowledge / / Bente Elkjaer, Maja Marie Lotz, Niels Christian Mossfeldt Nickelsen, editors
Current practices in workplace and organizational learning : revisiting the classics and advancing knowledge / / Bente Elkjaer, Maja Marie Lotz, Niels Christian Mossfeldt Nickelsen, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (261 pages)
Disciplina 658.3124
Soggetto topico Organizational learning
Knowledge management
Professional education
Aprenentatge organitzatiu
Aprenentatge cooperatiu
Gestió del coneixement
Soggetto genere / forma Llibres electrònics
ISBN 3-030-85060-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910520082803321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Daily knowledge valuation in organizations : traceability and capitalization / / Nada Matta, Hassan Atifi, Guillaume Ducellier
Daily knowledge valuation in organizations : traceability and capitalization / / Nada Matta, Hassan Atifi, Guillaume Ducellier
Autore Matta Nada
Edizione [1st edition]
Pubbl/distr/stampa London, England ; ; Hoboken, New Jersey : , : iSTE : , : Wiley, , 2016
Descrizione fisica 1 online resource (181 p.)
Disciplina 658.3124
Collana Cognitive Science Series
THEi Wiley ebooks
Soggetto topico Organizational learning
Knowledge management
ISBN 1-119-29215-8
1-119-29213-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Table of Contents; Title; Copyright; Preface; 1 Daily Knowledge; 1.1. Knowledge; 1.2. Daily knowledge; 1.3. Individual versus collaborative knowledge; 1.4. Challenge to manage daily knowledge; 1.5. Conclusions; 1.6. Bibliography; 2 Traceability; 2.1. Traces; 2.2. Profiling approaches; 2.3. Traceability of information; 2.4. Traceability of knowledge; 2.5. Conclusions; 2.6. Bibliography; 3 Traceability and Structuring of Decision-making; 3.1. Decision-making; 3.2. Cooperative decision-making; 3.3. Conflict management; 3.4. Conflict types; 3.5. Traceability of design rationale
3.6. Integrating traceability in PLM tools3.7. Conclusions; 3.8. Bibliography; 4 Classifications and Aggregation of Traces; 4.1. Classification; 4.2. Cooperative knowledge aggregation; 4.3. CKD classification algorithms; 4.4. Conclusions; 4.5. Bibliography; 5 Example of Traceability and Classifications of Decision-making; 5.1. Example of software design projects; 5.2. Example of PLM system design; 5.3. Example of ecodesign projects; 5.4. Conclusion; 5.5. Bibliography; 6 Communication, CMC and E-mail: A Brief Survey; 6.1. Introduction; 6.2. What is communication?
6.3. The pragmatics of interactions6.4. Pragmatics and speech acts; 6.5. Computer-mediated communication; 6.6. CMC, e-mail and knowledge management; 6.7. Conclusions; 6.8. Bibliography; 7 Traceability of Communications in Software Design; 7.1. Introduction; 7.2. Problem-solving; 7.3. Software development process; 7.4. Related works on e-mail analysis; 7.5. Project knowledge extraction from e-mails; 7.6. Example; 7.7. Context-aware algorithm; 7.8. Conclusion; 7.9. Bibliography; 8 Traceability of Actions in Crisis Management; 8.1. Introduction; 8.2. Crisis management
8.3. Decision-making in crisis situations8.4. Decision-making support using experience feedback; 8.5. Clever crisis management system (CCS) framework; 8.6. Traceability of the experience feedback; 8.7. Human-machine interface of CCS; 8.8. Example; 8.9. Conclusion; 8.10. Bibliography; 9 Traceability in Problem-solving Processes; 9.1. Introduction; 9.2. Problem-solving processes; 9.3. Traceability and reuse; 9.4. ProWhy; 9.5. Conclusions; 9.6. Bibliography; Conclusion; List of Authors; Index; End User License Agreement
Record Nr. UNINA-9910136913903321
Matta Nada  
London, England ; ; Hoboken, New Jersey : , : iSTE : , : Wiley, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Daily knowledge valuation in organizations : traceability and capitalization / / Nada Matta, Hassan Atifi, Guillaume Ducellier
Daily knowledge valuation in organizations : traceability and capitalization / / Nada Matta, Hassan Atifi, Guillaume Ducellier
Autore Matta Nada
Edizione [1st edition]
Pubbl/distr/stampa London, England ; ; Hoboken, New Jersey : , : iSTE : , : Wiley, , 2016
Descrizione fisica 1 online resource (181 p.)
Disciplina 658.3124
Collana Cognitive Science Series
THEi Wiley ebooks
Soggetto topico Organizational learning
Knowledge management
ISBN 1-119-29215-8
1-119-29213-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Table of Contents; Title; Copyright; Preface; 1 Daily Knowledge; 1.1. Knowledge; 1.2. Daily knowledge; 1.3. Individual versus collaborative knowledge; 1.4. Challenge to manage daily knowledge; 1.5. Conclusions; 1.6. Bibliography; 2 Traceability; 2.1. Traces; 2.2. Profiling approaches; 2.3. Traceability of information; 2.4. Traceability of knowledge; 2.5. Conclusions; 2.6. Bibliography; 3 Traceability and Structuring of Decision-making; 3.1. Decision-making; 3.2. Cooperative decision-making; 3.3. Conflict management; 3.4. Conflict types; 3.5. Traceability of design rationale
3.6. Integrating traceability in PLM tools3.7. Conclusions; 3.8. Bibliography; 4 Classifications and Aggregation of Traces; 4.1. Classification; 4.2. Cooperative knowledge aggregation; 4.3. CKD classification algorithms; 4.4. Conclusions; 4.5. Bibliography; 5 Example of Traceability and Classifications of Decision-making; 5.1. Example of software design projects; 5.2. Example of PLM system design; 5.3. Example of ecodesign projects; 5.4. Conclusion; 5.5. Bibliography; 6 Communication, CMC and E-mail: A Brief Survey; 6.1. Introduction; 6.2. What is communication?
6.3. The pragmatics of interactions6.4. Pragmatics and speech acts; 6.5. Computer-mediated communication; 6.6. CMC, e-mail and knowledge management; 6.7. Conclusions; 6.8. Bibliography; 7 Traceability of Communications in Software Design; 7.1. Introduction; 7.2. Problem-solving; 7.3. Software development process; 7.4. Related works on e-mail analysis; 7.5. Project knowledge extraction from e-mails; 7.6. Example; 7.7. Context-aware algorithm; 7.8. Conclusion; 7.9. Bibliography; 8 Traceability of Actions in Crisis Management; 8.1. Introduction; 8.2. Crisis management
8.3. Decision-making in crisis situations8.4. Decision-making support using experience feedback; 8.5. Clever crisis management system (CCS) framework; 8.6. Traceability of the experience feedback; 8.7. Human-machine interface of CCS; 8.8. Example; 8.9. Conclusion; 8.10. Bibliography; 9 Traceability in Problem-solving Processes; 9.1. Introduction; 9.2. Problem-solving processes; 9.3. Traceability and reuse; 9.4. ProWhy; 9.5. Conclusions; 9.6. Bibliography; Conclusion; List of Authors; Index; End User License Agreement
Record Nr. UNINA-9910808949603321
Matta Nada  
London, England ; ; Hoboken, New Jersey : , : iSTE : , : Wiley, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Dal knowledge management alla e-enterprise : le organizzazioni nell'era di Internet / Giuseppe Iacono ; prefazione di Claudio Demattè
Dal knowledge management alla e-enterprise : le organizzazioni nell'era di Internet / Giuseppe Iacono ; prefazione di Claudio Demattè
Autore Iacono, Giuseppe
Pubbl/distr/stampa Milano : Angeli, 2001
Descrizione fisica 165 p. : ill. ; 23 cm
Disciplina 658.054678
Altri autori (Persone) Demattè, Claudio
Collana Formazione permanente. Problemi d'oggi ; 232
Soggetto topico Knowledge management
Tecnologia dell'informazione - Effetti sull'organizzazione aziendale
ISBN 884642977X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991003317859707536
Iacono, Giuseppe  
Milano : Angeli, 2001
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Data analytics and management in data intensive domains : 22nd International Conference, DAMDID/RCDL 2020, Voronezh, Russia, October 13-16, 2020, selected proceedings / / Alexander Sychev, Sergey Makhortov, Bernhard Thalheim, editors
Data analytics and management in data intensive domains : 22nd International Conference, DAMDID/RCDL 2020, Voronezh, Russia, October 13-16, 2020, selected proceedings / / Alexander Sychev, Sergey Makhortov, Bernhard Thalheim, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (241 pages)
Disciplina 658.4038
Collana Communications in Computer and Information Science
Soggetto topico Management information systems
Knowledge management
ISBN 3-030-81200-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910492147803321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data analytics and management in data intensive domains : 22nd International Conference, DAMDID/RCDL 2020, Voronezh, Russia, October 13-16, 2020, selected proceedings / / Alexander Sychev, Sergey Makhortov, Bernhard Thalheim, editors
Data analytics and management in data intensive domains : 22nd International Conference, DAMDID/RCDL 2020, Voronezh, Russia, October 13-16, 2020, selected proceedings / / Alexander Sychev, Sergey Makhortov, Bernhard Thalheim, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (241 pages)
Disciplina 658.4038
Collana Communications in Computer and Information Science
Soggetto topico Management information systems
Knowledge management
ISBN 3-030-81200-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464391603316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Data and Information Quality [[electronic resource] ] : Dimensions, Principles and Techniques / / by Carlo Batini, Monica Scannapieco
Data and Information Quality [[electronic resource] ] : Dimensions, Principles and Techniques / / by Carlo Batini, Monica Scannapieco
Autore Batini Carlo
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Descrizione fisica 1 online resource (519 p.)
Disciplina 004
Collana Data-Centric Systems and Applications
Soggetto topico Database management
Data structures (Computer science)
Application software
Health informatics
Knowledge management
Database Management
Data Structures and Information Theory
Information Systems Applications (incl. Internet)
Health Informatics
Knowledge Management
ISBN 3-319-24106-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to Information Quality -- Data Quality Dimensions -- Information Quality Dimensions for Maps and Texts -- Data Quality Issues in Linked open data -- Quality Of Images -- Models for Information Quality -- Activities for Information Quality -- Object Identification -- Recent Advances in Object Identification -- Data Quality Issues in Data Integration Systems -- Information Quality in Use -- Methodologies for Information Quality Assessment and Improvement -- Information Quality in Healthcare -- Quality of Web Data and Quality of Big Data: Open Problems -- References -- Index.
Record Nr. UNINA-9910254981803321
Batini Carlo  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Data Governance : From the Fundamentals to Real Cases / / Ismael Caballero and Mario Piattini, editors
Data Governance : From the Fundamentals to Real Cases / / Ismael Caballero and Mario Piattini, editors
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (255 pages)
Disciplina 658.4038
Soggetto topico Knowledge management
Management information systems
ISBN 3-031-43773-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword by Yang Lee -- Foreword by Alberto Palomo -- Preface -- Overview -- Organization -- Part I: Data Governance Fundamentals -- Part II: Data Governance Applied -- Target Readership -- Acknowledgments -- Contents -- Contributors -- List of Abbreviations -- Part I: Data Governance Fundamentals -- Chapter 1: Introduction to Data Governance: A Bespoke Program Is Required for Success -- 1.1 Chapter Overview -- 1.2 Why Does Data Need to Be Governed? -- 1.2.1 Long-Lasting Consequences of Poor Data Decisions? -- 1.2.2 Mounting Data Debt -- 1.3 Who Needs to Be Involved in DG? -- 1.4 When Is It Appropriate for Organizations to Invest in DG? -- 1.5 Where Should Organizations Get Started with DG? -- 1.6 How Should Organizations Apportion Their DG Efforts Over Time? -- 1.6.1 Data Debt´s Impact -- 1.6.2 Proactive Versus Reactive DG -- 1.6.3 MacGyver Abilities -- 1.7 What Organizational Needs Does DG Fill? -- 1.7.1 Improving the Ways That Data Is Treated as an Asset? -- 1.7.2 Available but Not Widely Known Research Results -- 1.7.3 Using Data to Better Support the Organizational Mission -- 1.7.4 The Role of DG Frameworks -- 1.7.4.1 Related Term Definitions -- 1.7.4.2 A Small Concentrated Team Is Preferred Over Distributed (Dissipated) Knowledge -- 1.7.5 Using Data Strategically -- 1.7.5.1 Strategy Is About Why -- 1.7.5.2 What Is Data Strategy? -- 1.7.5.3 Working Together: Data and Organizational Strategy? -- 1.7.5.4 Strategic Commitment: Program Versus Project Focus -- 1.7.5.5 Digitizationing -- 1.7.5.6 A Watchful Eye Toward the US Federal Government (FEPA) -- 1.7.6 Breaking Through the Barriers of Data Governance -- 1.8 Chapter Summary -- Chapter 2: Data Strategy and Policies: The Role of Data Governance in Data Ecosystems -- 2.1 Introduction -- 2.2 Data Strategy and Policies -- 2.2.1 Data Strategy Fundamentals.
2.2.2 From Defensive to Offensive Data Strategy -- 2.2.3 Data Policies -- 2.3 New Development Trajectories for Data Governance -- 2.3.1 Data as Strategic Asset for Organizations -- 2.3.2 The Emergence of Data Ecosystems -- 2.4 Widening the Scope of Data Governance Operations -- 2.4.1 Consideration of Challenging External Influencing Factors -- 2.4.2 Bridging the Intra-organizational Perspective on Data Governance with the Inter-organizational Perspective -- 2.5 Utilizing Data Ecosystems as Part of Data Strategy -- 2.5.1 The Role of Ecosystem Data Governance -- 2.5.2 Inter-organizational Data Governance Modes -- 2.5.3 Adequate Positioning for Engaging in Data Ecosystems -- 2.6 Recommendations for Action -- 2.6.1 Recommendations for Actions for Single Organizations -- 2.6.2 Recommendations for Actions for Data Ecosystem Design -- References -- Chapter 3: Human Resources Management and Data Governance Roles: Executive Sponsor, Data Governors, and Data Stewards -- 3.1 Introduction -- 3.2 The Role of Human Resources in Data Governance -- 3.3 Understanding the Structure of the Data Governance Organization -- 3.3.1 Executive Steering Committee -- 3.3.2 Data Governance Board -- 3.3.3 Data Stewardship Council -- 3.3.4 Data Governance Program Office (DGPO) -- 3.3.4.1 Data Governance Program Office (DGPO) Responsibilities -- 3.3.4.2 Data Governance Manager Responsibilities -- 3.3.4.3 Enterprise Data Steward Responsibilities -- 3.4 Key Roles and Responsibilities for Data Stewards -- 3.4.1 Business Data Stewards -- 3.4.2 Technical Data Stewards -- 3.4.3 Operational Data Stewards -- 3.4.4 Project Data Stewards -- 3.5 Summary -- Chapter 4: Data Value and Monetizing Data -- 4.1 Managing Data as an Actual Asset -- 4.1.1 The Emergence of the Chief Data Officer -- 4.1.2 Approaches to Data Asset Management -- 4.1.3 Data´s Emergence as a Real Economic Asset.
4.1.4 The Need for Senior Executive Understanding -- 4.2 Impediments to Maturity in Enterprise Data Management -- 4.2.1 Leadership Issues -- 4.2.2 IM Priorities Over Which You Have Control or Influence -- 4.2.3 Resources Needed to Advance Data Management Capabilities -- 4.2.4 Negative Cultural Attitudes About Data Management -- 4.2.5 Overcoming the Barriers to Data Asset Management -- 4.2.6 Moving Forward -- 4.3 Generally Agreed-Upon Data Principles (GAIP) -- 4.4 Data Supply Chains and Ecosystems -- 4.4.1 Adapting the SCOR Model -- 4.4.2 Metrics for the Data Supply Chain -- 4.5 A New Model for the Data Supply Chain -- 4.6 Data Ecosystems -- 4.6.1 Data Within an Ecosystem -- 4.6.2 Ecosystem Entities -- 4.6.3 Ecosystem Features -- 4.6.4 Ecosystem Processes -- 4.6.5 Ecosystem Influences -- 4.6.6 Ecosystem Management -- 4.7 Applying Sustainability Concepts to Managing Data -- 4.8 Data Management Standards -- 4.8.1 Adapting IT Asset Management (ITAM) to Data Management -- 4.8.2 Adapting ITIL to Data Management -- 4.8.3 Adaptations from RIM and ECM -- 4.8.4 Adaptations from Library Science -- 4.8.5 Adaptations from Physical Asset Management -- 4.8.6 Adaptations from Financial Management -- Chapter 5: Data Governance Methodologies: The CC CDQ Reference Model for Data and Analytics Governance -- 5.1 Introduction -- 5.2 Paradigm Shifts in Data Governance: From Control to Value Creation -- 5.2.1 Data Governance: Definition and Mechanisms -- 5.2.2 Data Governance 1.0: Focus on Control, Data Quality, and Regulatory Compliance -- 5.2.3 Data Governance 2.0: Extending Beyond Control to Enable Value Creation -- 5.2.4 Need for Guidelines Supporting Data and Analytics Governance -- 5.3 The CC CDQ Reference Model for Data and Analytics Governance -- 5.3.1 Data Governance as Key Theme in the Competence Center Corporate Data Quality.
5.3.2 Design Principles for Data and Analytics Governance -- 5.3.2.1 Principle 1: Governance Linking Strategy to Operations -- 5.3.2.2 Principle 2: Federated Data Governance Involving Data and Analytics, Business, and IT Experts -- 5.3.2.3 Overview of the CC CDQ Reference Model for Data and Analytics Governance -- 5.4 Step 1: Set the Scope for Data and Analytics Governance -- 5.4.1 End-to-End Perspective for Defining Scope and Requirements -- 5.4.2 Data and Analytics Products and Their Information Supply Chains -- 5.5 Step 2: Who to Govern? - Processes, Roles, and Responsibilities -- 5.5.1 Decision Areas (Processes) -- 5.5.2 Data and Analytics Roles -- 5.5.2.1 Data Management Roles and Responsibilities -- 5.5.2.2 Analytics Roles and Responsibilities -- 5.5.2.3 Organization-Wide Coordination of Data and Analytics -- 5.5.3 Assigning Roles to Responsibilities -- 5.6 Step 3: How to Govern? - Deriving the Operating Model -- 5.6.1 Mapping Roles, Responsibilities, and Processes to the Organizational Context -- 5.6.1.1 Typical Configurations -- 5.7 Summary -- References -- Chapter 6: Data Governance Tools -- 6.1 Introduction -- 6.2 The Business Need for Data Governance and Its Importance -- 6.2.1 Common Business Outcomes Led by Chief Data Officers -- 6.3 Case Study: Southwest Airlines and the Role of Technology on Business Outcomes -- 6.3.1 Data Challenges in the Transportation Industry -- 6.4 Key Functionalities Needed in the Data Governance Tools -- 6.4.1 Twelve Technology Features Chief Data Officers Can Use to Become Data-Driven -- 6.4.2 Data Governance Technology Challenges -- 6.5 Four Must-Have Technology Focus Areas to Kick-start Data Governance -- 6.5.1 Flexible Operating Model -- 6.5.1.1 Insurance Customer Story -- 6.5.2 Identification of Data Domains -- 6.5.2.1 Financial Services Customer Story.
6.5.3 Identification of Critical Data Elements (CDEs) Within Data Domains -- 6.5.3.1 Federal Government Agency in Washington, D.C., Story -- 6.5.3.2 Technology Company Story -- 6.5.4 Enable Control Measurements -- 6.5.4.1 Technology Company Out of California Story -- 6.6 Conclusion -- Chapter 7: Maturity Models for Data Governance -- 7.1 Introduction -- 7.2 Maturity Models -- 7.2.1 DAMA -- 7.2.2 Aiken´s Model -- 7.2.3 Data Management Maturity (DMM) Model -- 7.2.4 IBM Model -- 7.2.5 Gartner´s Enterprise Information Management Model -- 7.2.6 DCAM -- 7.3 MAMD (Alarcos´ Model for Data Maturity) -- 7.3.1 ISO/IEC 33000 Standards Family -- 7.3.2 MAMD Overview -- 7.3.3 The Capability Dimension -- 7.3.4 Process Dimension -- 7.3.5 Organizational Maturity Model -- 7.4 Practical Applications of MAMD -- 7.4.1 Regional Government: Improving the Performance of Authentication Servers -- 7.4.2 Insurance Company: Building a ``Source of Truth´´ Repository -- 7.4.3 Bicycle Manufacturer: Enabling Better Analytics -- 7.4.4 Telco Company: Building a Data Marketplace -- 7.4.5 Hospital/Faculty of Medicine: Assessing the Organizational Maturity -- 7.4.6 University Library: Assessing the Organizational Maturity -- 7.4.7 DQIoT: Developing a MAMD-Based Maturity Model for IoT -- 7.4.8 Regional Institute of Statistics: Developing a MAMD-Based Model for the Official Statistics Domain -- 7.4.9 CODE.CLINIC: Tailoring MAMD for Coding Clinical Data -- References -- Part II: Data Governance Applied -- Chapter 8: Data Governance in the Banking Sector -- 8.1 Inception, Challenges, and Evolution -- 8.2 Data-Driven Bank -- 8.3 Data Stewardship -- 8.4 Single Data Marketplace Ecosystem (SDM) -- 8.5 DM& -- G Dashboard -- 8.5.1 Overview -- 8.5.2 Forecast -- 8.5.3 Data Value -- 8.6 Data as a Service (DaaS) -- 8.7 The Magic Algorithm -- Chapter 9: Data Has the Power to Transform Society.
9.1 Introduction.
Record Nr. UNINA-9910799479403321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Quality Management with Semantic Technologies [[electronic resource] /] / by Christian Fürber
Data Quality Management with Semantic Technologies [[electronic resource] /] / by Christian Fürber
Autore Fürber Christian
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Gabler, , 2016
Descrizione fisica 1 online resource (230 p.)
Disciplina 650
Soggetto topico Management information systems
Knowledge management
Business Information Systems
Knowledge Management
ISBN 3-658-12225-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword; 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
PART 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
3.5 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
4.2.4 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
6 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
7.3.1 Reuse of Existing Ontologies
Record Nr. UNINA-9910254942103321
Fürber Christian  
Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Gabler, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Data science and analytics for SMEs : consulting, tools, practical use cases / / Afolabi Ibukun Tolulope
Data science and analytics for SMEs : consulting, tools, practical use cases / / Afolabi Ibukun Tolulope
Autore Tolulope Afolabi Ibukun
Pubbl/distr/stampa New York, NY : , : Apress, , [2022]
Descrizione fisica 1 online resource (341 pages)
Disciplina 658.4038
Soggetto topico Business requirements analysis
Knowledge management
Small business
ISBN 1-4842-8670-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Preface -- Chapter 1: Introduction -- 1.1 Data Science -- 1.2 Data Science for Business -- 1.3 Business Analytics Journey -- Events in Real Life and Description -- Capturing the Data -- Accessible Location and Storage -- Extracting Data for Analysis -- Data Analytics -- Summarize and Interpret Results -- Presentation -- Recommendations, Strategies, and Plan -- Implementation -- 1.4 Small and Medium Enterprises (SME) -- 1.5 Business Analytics in Small Business -- 1.6 Types of Analytics Problems in SME -- 1.7 Analytics Tools for SMES -- 1.8 Road Map to This Book -- Using RapidMiner Studio -- Using Gephi -- 1.9 Problems -- 1.10 References -- Chapter 2: Data for Analysis in Small Business -- 2.1 Source of Data -- Data Privacy -- 2.2 Data Quality and Integrity -- 2.3 Data Governance -- 2.4 Data Preparation -- Summary Statistics -- Example 2.1 -- Missing Data -- Data Cleaning - Outliers -- Normalization and Categorical Variables -- Handling Categorical Variables -- 2.5 Data Visualization -- 2.6 Problems -- 2.7 References -- Chapter 3: Business Analytics Consulting -- 3.1 Business Analytics Consulting -- 3.2 Managing Analytics Project -- 3.3 Success Metrics in Analytics Project -- 3.4 Billing the Analytics Project -- 3.5 References -- Chapter 4: Business Analytics Consulting Phases -- 4.1 Proposal and Initial Analysis -- 4.2 Pre-engagement Phase -- 4.3 Engagement Phase -- 4.4 Post-Engagement Phase -- 4.5 Problems -- 4.6 References -- Chapter 5: Descriptive Analytics Tools -- 5.1 Introduction -- 5.2 Bar Chart -- 5.3 Histogram -- 5.4 Line Graphs -- 5.5 Boxplots -- 5.6 Scatter Plots -- 5.7 Packed Bubble Charts -- 5.8 Treemaps -- 5.9 Heat Maps -- 5.10 Geographical Maps -- 5.11 A Practical Business Problem I (Simple Descriptive Analytics) -- 5.12 Problems.
5.13 References -- Chapter 6: Predicting Numerical Outcomes -- 6.1 Introduction -- 6.2 Evaluating Prediction Models -- 6.3 Practical Business Problem II (Sales Prediction) -- 6.4 Multiple Linear Regression -- 6.5 Regression Trees -- 6.6 Neural Network (Prediction) -- 6.7 Conclusion on Sales Prediction -- 6.8 Problems -- 6.9 References -- Chapter 7: Classification Techniques -- 7.1 Classification Models and Evaluation -- 7.2 Practical Business Problem III (Customer Loyalty) -- 7.3 Neural Network -- 7.4 Classification Tree -- 7.5 Random Forest and Boosted Trees -- 7.6 K-Nearest Neighbor -- 7.7 Logistic Regression -- 7.8 Problems -- 7.9 References -- Chapter 8: Advanced Descriptive Analytics -- 8.1 Clustering -- 8.2 K-Means -- 8.3 Practical Business Problem IV (Customer Segmentation) -- 8.4 Association Analysis -- 8.5 Network Analysis -- 8.6 Practical Business Problem V (Staff Efficiency) -- 8.7 Problems -- 8.8 References -- Chapter 9: Case Study Part I -- 9.1 SME Ecommerce -- 9.2 Introduction to SME Case Study -- 9.3 Initial Analysis -- 9.4 Analytics Approach -- 9.5 Pre-engagement -- 9.6 References -- Chapter 10: Case Study Part II -- 10.1 Goal 1: Increase Website Traffic -- 10.2 Goal 2: Increase Website Sales Revenue -- 10.3 Problems -- 10.4 References -- Data Files -- Index.
Record Nr. UNINA-9910616397403321
Tolulope Afolabi Ibukun  
New York, NY : , : Apress, , [2022]
Materiale a stampa
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