Info
Designing data spaces : the ecosystem approach to competitive advantage / / editors, Boris Otto, Michael Ten Hompel, Stefan Wrobel
| Designing data spaces : the ecosystem approach to competitive advantage / / editors, Boris Otto, Michael Ten Hompel, Stefan Wrobel |
| Autore | Otto Boris |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (xv, 580 pages) : illustrations (chiefly color) |
| Altri autori (Persone) |
OttoBoris
ten HompelMichael WrobelStefan |
| Soggetto topico |
Database management
Information technology |
| Soggetto non controllato |
Data Spaces
GAIA-X Data Lakes Big Data Information Retrieval Information Systems Applications Data Ecosystems Data Integration Data Security |
| ISBN | 3-030-93975-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Contents -- Abbreviation -- Part I: Foundations and Context -- Chapter 1: The Evolution of Data Spaces -- 1.1 Data Sharing in Data Ecosystems -- 1.1.1 The Role of Data for Enterprises -- 1.1.2 Data Sharing and Data Sovereignty -- 1.1.3 Example Mobility Data Space -- 1.1.4 Need for Action and Research Goal -- 1.2 Conceptual and Technological Foundations -- 1.2.1 Data Spaces Defined -- 1.2.2 Roles and Responsibilities in Data Spaces -- 1.2.3 GAIA-X and IDS -- 1.3 Evolutionary Stages of Data Space Ecosystems -- 1.4 Designing Data Spaces -- 1.4.1 Ecosystem Perspective -- 1.4.2 Federator Perspective -- 1.5 Summary and Outlook -- References -- Chapter 2: How to Build, Run, and Govern Data Spaces -- 2.1 Data Space Design Principles -- 2.1.1 Entirely New Services for Users Based on Enhanced Transparency and Data Sovereignty -- 2.1.2 Level Playing Field for Data Sharing and Exchange -- 2.1.3 Need for Data Space Interoperability: The Soft Infrastructure -- 2.1.4 Public-Private Governance: Europe Taking the Lead in Establishing the Soft Infrastructure in a Coordinated and Collabora... -- 2.2 Building Blocks for Data Spaces -- 2.2.1 Technical Building Blocks -- 2.2.2 Governance Building Blocks -- 2.3 Synthesis of Building Blocks to Data Spaces -- 2.4 Harmonized Approach to Data Space Governance -- 2.5 The Way Forward and Convergence: Actions to Take in the Coming Digital Decade -- References -- Chapter 3: International Data Spaces in a Nutshell -- 3.1 International Data Spaces -- 3.1.1 Goals of the International Data Spaces -- 3.1.2 Reference Architecture Model -- 3.1.2.1 The International Data Spaces Components -- 3.1.2.2 The International Data Spaces Roles -- 3.1.2.3 Usage Control -- 3.1.3 Certification -- 3.1.3.1 Security Profiles -- 3.1.3.2 Participant Certification -- 3.1.3.3 Component Certification -- 3.1.4 Open Source.
References -- Chapter 4: Role of Gaia-X in the European Data Space Ecosystem -- 4.1 A Quick Introduction to Gaia-X -- 4.2 The Business World with Gaia-X -- 4.2.1 Economy of Data -- 4.2.2 Compliance -- 4.2.3 Measuring Success -- 4.3 The Gaia-X Principles -- 4.3.1 Objectives -- 4.3.2 Policy Rules and Specifications for Infrastructure Application and Data -- 4.3.3 Federated Services in Business Ecosystems -- 4.4 The Gaia-X Data Spaces -- 4.4.1 Finance and Insurance -- 4.4.2 Energy -- 4.4.3 Automotive -- 4.4.4 Health -- 4.4.5 Aeronautics -- 4.4.6 Travel -- 4.5 The National Hub Organization and the Launching of Additional Data Spaces -- 4.6 Conclusion: Data Spaces-The Enabler of Digital in Business -- References -- Chapter 5: Legal Aspects of IDS: Data Sovereignty-What Does It Imply? -- 5.1 Data Sovereignty: Freedom of Contract and Regulation -- 5.1.1 No Ownership or Exclusivity Rights in Data -- 5.1.2 Usage Control: Legally and Technically -- 5.1.3 Database Rights -- 5.1.4 Trade Secrets -- 5.1.5 Competition Law -- 5.1.6 EU Strategy on Data: The Relevance of Data Spaces -- 5.1.7 Data Governance Act: First Comments -- 5.1.8 Personal and Non-personal Data -- 5.1.8.1 GDPR -- 5.1.8.2 Free Flow of Non-Personal Data Regulation -- 5.1.9 Cybersecurity -- 5.1.9.1 NIS Directive -- 5.1.9.2 Cybersecurity Act -- 5.2 Preparing Contractual Ecosystems -- 5.2.1 Platform Contracts -- 5.2.1.1 Key Principles -- 5.2.1.2 Legal TestBed: A Lead Example -- 5.2.2 Data Licensing Agreements -- 5.2.2.1 The Contract Matrix -- 5.2.2.2 The IDS Sample Contracts -- 5.3 Implementing Compliance -- 5.3.1 GDPR -- 5.3.1.1 Controllers, Joint Controllers, and Processors -- 5.3.1.2 Documentation -- 5.3.1.3 Breach Notifications -- 5.3.1.4 Enforcement and Sanctions -- 5.3.2 Competition Law -- 5.4 Certifications from a Legal Perspective -- 5.4.1 Role of Procedural Rules -- 5.4.2 Additional Aspects. Chapter 6: Tokenomics: Decentralized Incentivization in the Context of Data Spaces -- 6.1 Tokenomics in the Context of Data Spaces -- 6.2 Token-Based Supply Chain Management -- 6.2.1 Supply Chain Traceability -- 6.2.2 Distributed Ledger Technology and Tokenomics -- 6.2.3 DLT-Based Supply Chain Traceability -- 6.3 Tokenomics in the Context of Personal Data Markets -- 6.3.1 Personal Data Markets -- 6.3.2 Motivational Factors for Tokenomics Approach in Personal Data Markets -- 6.3.3 Token Design Principles for Personal Data Markets -- 6.3.4 Derivation of Token Archetypes for PDMs -- 6.4 Conclusions -- References -- Part II: Data Space Technologies -- Chapter 7: The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange -- 7.1 Introduction -- 7.2 Evolving Trust in the IDS Toward Self-Sovereign Identity -- 7.3 Definition of Contract Clauses: The IDS Usage Contract Language and Its Core Concepts -- 7.3.1 The Solid Access Control Model vs. IDS Usage Contract Language -- 7.3.2 Usage Control Dimensions -- 7.3.3 Operators for Usage Control Rules -- 7.4 The Policy Information Point -- 7.5 The Participant Information Service (ParIS) -- 7.6 Conclusion: The IDS-IM as the Bridge Between Expressions, Infrastructure, and Enforcement -- References -- Chapter 8: Data Usage Control -- 8.1 Introduction -- 8.2 Usage Control -- 8.2.1 Access Control -- 8.2.2 Usage Control -- 8.2.3 Usage Control Components and Communication Flow -- 8.2.4 Specification, Management, and Negotiation -- 8.2.5 Related Concepts -- 8.2.5.1 Data Leak/Loss Prevention -- 8.2.5.2 Digital Rights Management -- 8.2.5.3 User Managed Access -- 8.2.5.4 Windows Information Protection -- 8.3 Usage Control in the IDS -- 8.3.1 Usage Control Policies -- 8.3.1.1 Policy Classes -- 8.3.1.2 Policy Negotiation -- 8.3.2 Usage Control Technologies -- 8.3.2.1 Integration Concept. 8.3.2.2 MY DATA Control Technologies -- 8.3.3 Logic-Based Usage Control (LUCON) -- 8.3.3.1 Degree (D) -- 8.3.3.2 Data Provenance Tracking -- 8.4 Conclusion -- References -- Chapter 9: Building Trust in Data Spaces -- 9.1 Introduction -- 9.2 Data Sovereignty and Usage Control -- 9.2.1 Data Provider and Data Consumer -- 9.2.2 Protection Goals and Attacker Model -- 9.2.3 Building Blocks -- 9.3 Certification Process -- 9.3.1 Multiple Eye Principle -- 9.3.2 Component Certification -- 9.3.3 Operational Environment Certification -- 9.4 Connector Identities and Software Signing -- 9.4.1 Technical Implementation of the Certification Process -- 9.4.2 Connector Identities and Company Descriptions -- 9.4.3 Software Signing and Manifests -- 9.5 Connector System Security -- 9.5.1 Trusted Computing Base -- 9.5.2 Remote Attestation -- 9.6 Conclusion -- References -- Chapter 10: Blockchain Technology and International Data Spaces -- 10.1 Introduction -- 10.2 Blockchain Technology -- 10.2.1 Basic Concept -- 10.2.2 Design Parameters -- 10.2.3 Smart Contracts -- 10.2.4 Opportunities of Blockchain Systems -- 10.3 Blockchain in International Data Spaces -- 10.4 Application Examples: Industrial Use Cases -- 10.4.1 TrackChain -- 10.4.2 Silke -- 10.4.3 Sinlog -- 10.4.4 BC for Production -- 10.5 Conclusion -- References -- Chapter 11: Federated Data Integration in Data Spaces -- 11.1 Introduction -- 11.2 Federated Data Integration Workflows in Data Spaces -- 11.2.1 A Simple Demonstrator Scenario -- 11.2.2 A Data Integration Workflow Solution for Data Spaces -- 11.3 Toward Formalisms for Virtual Data Space Integration -- 11.3.1 Logical Foundations for Data Integration -- 11.3.2 Data Integration Tool Extensions for Data Spaces -- References -- Chapter 12: Semantic Integration and Interoperability -- 12.1 Introduction -- 12.2 The Neglected Variety Dimension. 12.2.1 From Big Data to Cognitive Data -- 12.3 Representing Knowledge in Semantic Graphs -- 12.3.1 Representing Data Semantically -- 12.4 RDF a Holistic Data Representation for Schema, Data, and Metadata -- 12.5 Establishing Interoperability by Linking and Mapping between Different Data and Knowledge Representations -- 12.6 Exemplary Data Integration in Supply Chains with ScorVoc -- 12.7 Conclusions -- References -- Chapter 13: Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning -- 13.1 Introduction -- 13.2 Big Data, Machine Learning, and Artificial Intelligence -- 13.3 An Open Platform for Developing AI Applications -- 13.4 Machine Learning at the Edge -- 13.5 Machine Learning in Digital Ecosystems -- 13.6 Trustworthy AI Solutions -- 13.7 Summary -- References -- Chapter 14: IDS as a Foundation for Open Data Ecosystems -- 14.1 Introduction -- 14.2 Barriers of Open Data -- 14.3 Related Work -- 14.4 International Data Spaces and Open Data -- 14.4.1 IDS as an Open Data Technology -- 14.4.2 IDS Components in an Open Data Environment -- 14.4.3 Benefits -- 14.5 The Public Data Space -- 14.5.1 The Open Data Connector -- 14.5.2 The Open Data Broker -- 14.5.3 Use Case: Publishing Open Government Data -- 14.6 Discussion and Conclusion -- References -- Chapter 15: Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure -- 15.1 Introduction -- 15.2 European Research Area -- 15.2.1 European Research Infrastructures and ESFRI Roadmap -- 15.2.2 European Open Science Cloud (EOSC) -- 15.3 Technology-Driven Science Transformation -- 15.3.1 Science Digitalization and Industry 4.0 -- 15.3.2 Transformational Role of Artificial Intelligence -- 15.3.3 Promises of 5G Technologies -- 15.3.4 Adopting Platform and Ecosystems Business Model for Future SDI. 15.3.5 Other Infrastructure Technologies and Trends. |
| Record Nr. | UNISA-996483157003316 |
Otto Boris
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Designing Data Spaces : The Ecosystem Approach to Competitive Advantage / / edited by Boris Otto, Michael ten Hompel, Stefan Wrobel
| Designing Data Spaces : The Ecosystem Approach to Competitive Advantage / / edited by Boris Otto, Michael ten Hompel, Stefan Wrobel |
| Autore | Otto Boris |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (xv, 580 pages) : illustrations (chiefly color) |
| Disciplina | 005.3 |
| Altri autori (Persone) |
OttoBoris
ten HompelMichael WrobelStefan |
| Collana | Computer Science Series |
| Soggetto topico |
Application software
Big data Expert systems (Computer science) Information storage and retrieval systems Computer and Information Systems Applications Big Data Knowledge Based Systems Information Storage and Retrieval |
| ISBN |
9783030939755
3030939758 |
| Classificazione | COM018000COM021000COM025000COM030000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Foundations and Context -- 1. The Evolution of Data Spaces -- 2. How to Build, Run, and Govern Data Spaces -- 3. International Data Spaces in a Nutshell -- 4. Role of Gaia-X in the European Data Space Ecosystem -- 5. Legal Aspects of IDS: Data Sovereignty—What Does It Imply? -- 6. Tokenomics: Decentralized Incentivization in the Context of Data Spaces -- Part II: Data Space Technologies -- 7. The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange -- 8. Data Usage Control -- 9. Building Trust in Data Spaces -- 10. Blockchain Technology and International Data Spaces -- 11. Federated Data Integration in Data Spaces -- 12. Semantic Integration and Interoperability -- 13. Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning -- 14. IDS as a Foundation for Open Data Ecosystems -- 15. Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure -- Part III: Use Cases and Data Ecosystems -- 16. Silicon Economy:Logistics as the Natural Data Ecosystem -- 17. Agricultural Data Space -- 18. Medical Data Spaces in Healthcare Data Ecosystems -- 19. Industrial Data Spaces -- 20. Energy Data Space -- 21. Mobility Data Space -- Part IV: Solutions and Applications -- 22. Data Sharing Spaces: The BDVA Perspective -- 23. Data Platform Solutions -- 24. FIWARE for Data Spaces -- 25. Sovereign Cloud Technologies for Scalable Data Spaces -- 26. Data Space Based on Mass Customization Model -- 27. Huawei and International Data Spaces -- International Collaboration Between Data Spaces and Carrier Networks -- 29. From Linear Supply Chains to Open Supply Ecosystems -- 30. Data Spaces: First Applications in Mobility and Industry -- 31. Competition, Security, and Transparency: Data in Connected Vehicles -- Data Space Functionality -- The Energy Data Space: The Path to a European Approach for Energy. |
| Record Nr. | UNINA-9910585784403321 |
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Otto Boris
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning: ECML-95 [[electronic resource] ] : 8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25 - 27, 1995. Proceedings / / edited by Nada Lavrač, Stefan Wrobel
| Machine Learning: ECML-95 [[electronic resource] ] : 8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25 - 27, 1995. Proceedings / / edited by Nada Lavrač, Stefan Wrobel |
| Edizione | [1st ed. 1995.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1995 |
| Descrizione fisica | 1 online resource (XII, 376 p.) |
| Disciplina | 006.3/1 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Artificial Intelligence Algorithm Analysis and Problem Complexity |
| ISBN | 3-540-49232-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Reasoning and learning in probabilistic and possibilistic networks: An overview -- Problem decomposition and the learning of skills -- Machine learning in the world wide web -- Abstract computer models: Towards a new method for theorizing about adaptive agents -- Learning abstract planning cases -- The role of prototypicality in exemplar-based learning -- Specialization of recursive predicates -- A distributed genetic algorithm improving the generalization behavior of neural networks -- Learning non-monotonic logic programs: Learning exceptions -- A comparative utility analysis of case-based reasoning and control-rule learning systems -- A minimization approach to propositional inductive learning -- On concept space and hypothesis space in case-based learning algorithms -- The power of decision tables -- Pruning multivariate decision trees by hyperplane merging -- Multiple-Knowledge Representations in concept learning -- The effect of numeric features on the scalability of inductive learning programs -- Analogical logic program synthesis from examples -- A guided tour through hypothesis spaces in ILP -- JIGSAW: Puzzling together RUTH and SPECTRE (Extended abstract) -- Discovery of constraints and data dependencies in relational databases (Extended abstract) -- Learning disjunctive normal forms in a dual classifier system (Extended abstract) -- The effects of noise on efficient incremental induction (Extended abstract) -- Analysis of Rachmaninoff's piano performances using inductive logic programming (Extended abstract) -- Handling real numbers in ILP: A step towards better behavioural clones (Extended abstract) -- Simplifying decision trees by pruning and grafting: New results (Extended abstract) -- A tight integration of pruning and learning (Extended abstract) -- Decision-tree based neural network (Extended abstract) -- Learning recursion with iterative bootstrap induction (Extended abstract) -- Patching proofs for reuse (Extended abstract) -- Adapting to drift in continuous domains (Extended abstract) -- Parallel recombinative reinforcement learning (Extended abstract) -- Learning to solve complex tasks for reactive systems (Extended abstract) -- Co-operative Reinforcement Learning by payoff filters (Extended abstract) -- Automatic synthesis of control programs by combination of learning and problem solving methods (Extended abstract) -- Analytical learning guided by empirical technology: An approach to integration (Extended abstract) -- A new MDL measure for robust rule induction (Extended abstract) -- Class-driven statistical discretization of continuous attributes (Extended abstract) -- Generating neural networks through the induction of threshold logic unit trees (Extended abstract) -- Learning classification rules using lattices (Extended abstract) -- Hybrid classification: Using axis-parallel and oblique subdivisions of the attribute space (Extended abstract) -- An induction-based control for genetic algorithms (Extended abstract) -- Fender: An approach to theory restructuring (extended abstract) -- Language series revisited: The complexity of hypothesis spaces in ILP (Extended abstract) -- Prototype, nearest neighbor and hybrid algorithms for time series classification (Extended abstract). |
| Record Nr. | UNISA-996466104703316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1995 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Proceedings, twenty-second International Conference on Machine Learning
| Proceedings, twenty-second International Conference on Machine Learning |
| Autore | Dzeroski Saso |
| Pubbl/distr/stampa | [Place of publication not identified], : ACM, 2005 |
| Descrizione fisica | 1 online resource (1113 pages) |
| Collana | ACM Other conferences |
| Soggetto topico |
Engineering & Applied Sciences
Computer Science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Proceedings of the 22nd International Conference on Machine Learning
ICML '05 |
| Record Nr. | UNINA-9910375922403321 |
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Dzeroski Saso
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| [Place of publication not identified], : ACM, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Visual Analytics for Data Scientists [[electronic resource] /] / by Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel
| Visual Analytics for Data Scientists [[electronic resource] /] / by Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel |
| Autore | Andrienko Natalia |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (450 pages) |
| Disciplina | 001.4226 |
| Soggetto topico |
Data mining
Artificial intelligence Pattern recognition Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition |
| ISBN | 3-030-56146-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Introduction to Visual Analytics in Data Science -- 1. Introduction to Visual Analytics by an Example -- 2. General Concepts -- 3. Principles of Interactive Visualisation -- 4. Computational Techniques in Visual Analytics -- Part II: Visual Analytics along the Data Science Workflow -- 5. Visual Analytics for Investigating and Processing Data -- 6. Visual Analytics for Understanding Multiple Attributes -- 7. Visual Analytics for Understanding Relationships between Entities -- 8. Visual Analytics for Understanding Temporal Distributions and Variations -- 9. Visual Analytics for Understanding Spatial Distributions and Spatial Variation -- 10. Visual Analytics for Understanding Phenomena in Space and Time -- 11. Visual Analytics for Understanding Texts -- 12. Visual Analytics for Understanding Images and Video -- 13. Computational Modelling with Visual Analytics -- 14. Conclusion. |
| Record Nr. | UNISA-996418270303316 |
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Andrienko Natalia
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Visual Analytics for Data Scientists / / by Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel
| Visual Analytics for Data Scientists / / by Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel |
| Autore | Andrienko Natalia |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (450 pages) |
| Disciplina | 001.4226 |
| Soggetto topico |
Data mining
Artificial intelligence Pattern perception Data Mining and Knowledge Discovery Artificial Intelligence Pattern Recognition |
| ISBN | 3-030-56146-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Introduction to Visual Analytics in Data Science -- 1. Introduction to Visual Analytics by an Example -- 2. General Concepts -- 3. Principles of Interactive Visualisation -- 4. Computational Techniques in Visual Analytics -- Part II: Visual Analytics along the Data Science Workflow -- 5. Visual Analytics for Investigating and Processing Data -- 6. Visual Analytics for Understanding Multiple Attributes -- 7. Visual Analytics for Understanding Relationships between Entities -- 8. Visual Analytics for Understanding Temporal Distributions and Variations -- 9. Visual Analytics for Understanding Spatial Distributions and Spatial Variation -- 10. Visual Analytics for Understanding Phenomena in Space and Time -- 11. Visual Analytics for Understanding Texts -- 12. Visual Analytics for Understanding Images and Video -- 13. Computational Modelling with Visual Analytics -- 14. Conclusion. |
| Record Nr. | UNINA-9910483123703321 |
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Andrienko Natalia
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Visual analytics of movement / / Gennady Andrienko [and four others]
| Visual analytics of movement / / Gennady Andrienko [and four others] |
| Autore | Andrienko Gennady |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Heidelberg [Germany] : , : Springer, , 2013 |
| Descrizione fisica | 1 online resource (xviii, 387 pages) : illustrations (some color) |
| Disciplina | 006.31 |
| Collana | Gale eBooks |
| Soggetto topico |
Visual analytics
Information visualization Data mining Pattern perception |
| ISBN | 3-642-37583-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Conceptual framework -- Transformations of movement data -- Visual analytics infrastructure -- Visual analytics focusing on movers -- Visual analytics focusing on spatial events -- Visual analytics focusing on space -- Visual analytics focusing on time -- Discussion and outlook. |
| Record Nr. | UNINA-9910739460903321 |
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Andrienko Gennady
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| Heidelberg [Germany] : , : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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