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Data Science for Economics and Finance [[electronic resource] ] : Methodologies and Applications / / edited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana
| Data Science for Economics and Finance [[electronic resource] ] : Methodologies and Applications / / edited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana |
| Autore | Consoli Sergio |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (XIV, 355 p. 56 illus., 44 illus. in color.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Machine learning Management information systems Big data Application software Information storage and retrieval Data Mining and Knowledge Discovery Machine Learning Business Information Systems Big Data/Analytics Computer Appl. in Administrative Data Processing Information Storage and Retrieval |
| Soggetto non controllato |
Data Mining and Knowledge Discovery
Machine Learning Business Information Systems Big Data/Analytics Computer Appl. in Administrative Data Processing Information Storage and Retrieval IT in Business Computer and Information Systems Applications Open Access Data Mining Big Data Data Analytics Decision Support Systems Semantics and Reasoning Expert systems / knowledge-based systems Business mathematics & systems Public administration Information technology: general issues Information retrieval Data warehousing |
| ISBN | 3-030-66891-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Science Technologies in Economics and Finance: A Gentle Walk-In -- Supervised Learning for the Prediction of Firm Dynamics -- Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting -- Machine Learning for Financial Stability -- Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms -- Classifying Counterparty Sector in EMIR Data -- Massive Data Analytics for Macroeconomic Nowcasting -- New Data Sources for Central Banks -- Sentiment Analysis of Financial News: Mechanics and Statistics -- Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies -- Extraction and Representation of Financial Entities from Text -- Quantifying News Narratives to Predict Movements in Market Risk -- Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets? -- Network Analysis for Economics and Finance: An application to Firm Ownership. |
| Record Nr. | UNISA-996464413703316 |
Consoli Sergio
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Data Warehousing and Knowledge Discovery [[electronic resource] ] : 7th International Conference, DaWak 2005, Copenhagen, Denmark, August 22-26, 2005, Proceedings / / edited by A Min Tjoa
| Data Warehousing and Knowledge Discovery [[electronic resource] ] : 7th International Conference, DaWak 2005, Copenhagen, Denmark, August 22-26, 2005, Proceedings / / edited by A Min Tjoa |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 |
| Descrizione fisica | 1 online resource (XVI, 544 p.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Data structures (Computer science)
Database management Information storage and retrieval Application software Computer communication systems Artificial intelligence Data Structures and Information Theory Database Management Information Storage and Retrieval Information Systems Applications (incl. Internet) Computer Communication Networks Artificial Intelligence |
| Soggetto non controllato |
Data warehousing
Knowledge discovery DaWaK |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Warehouse I -- A Tree Comparison Approach to Detect Changes in Data Warehouse Structures -- Extending the UML for Designing Association Rule Mining Models for Data Warehouses -- Event-Feeded Dimension Solution -- XML-OLAP: A Multidimensional Analysis Framework for XML Warehouses -- Data Warehouse II -- Graph-Based Modeling of ETL Activities with Multi-level Transformations and Updates -- Extending UML 2 Activity Diagrams with Business Intelligence Objects -- Automatic Selection of Bitmap Join Indexes in Data Warehouses -- Evaluating Data Warehouses and Tools -- A Survey of Open Source Tools for Business Intelligence -- DWEB: A Data Warehouse Engineering Benchmark -- A Set of Quality Indicators and Their Corresponding Metrics for Conceptual Models of Data Warehouses -- Design and Development of a Tool for Integrating Heterogeneous Data Warehouses -- Schema Transformations -- An Evolutionary Approach to Schema Partitioning Selection in a Data Warehouse -- Using Schema Transformation Pathways for Incremental View Maintenance -- Data Mapper: An Operator for Expressing One-to-Many Data Transformations -- Materialized Views -- Parallel Consistency Maintenance of Materialized Views Using Referential Integrity Constraints in Data Warehouses -- Selective View Materialization in a Spatial Data Warehouse -- PMC: Select Materialized Cells in Data Cubes -- Aggregates -- Progressive Ranking of Range Aggregates -- On Efficient Storing and Processing of Long Aggregate Lists -- Data Warehouse Queries and Database Processing Issues -- Ad Hoc Star Join Query Processing in Cluster Architectures -- A Precise Blocking Method for Record Linkage -- Flexible Query Answering in Data Cubes -- An Extendible Array Based Implementation of Relational Tables for Multi Dimensional Databases -- Data Mining Algorithms and Techniques -- Nearest Neighbor Search on Vertically Partitioned High-Dimensional Data -- A Machine Learning Approach to Identifying Database Sessions Using Unlabeled Data -- Hybrid System of Case-Based Reasoning and Neural Network for Symbolic Features -- Data Mining -- Spatio–temporal Rule Mining: Issues and Techniques -- Hybrid Approach to Web Content Outlier Mining Without Query Vector -- Incremental Data Mining Using Concurrent Online Refresh of Materialized Data Mining Views -- A Decremental Algorithm for Maintaining Frequent Itemsets in Dynamic Databases -- Association Rules -- Discovering Richer Temporal Association Rules from Interval-Based Data -- Semantic Query Expansion Combining Association Rules with Ontologies and Information Retrieval Techniques -- Maintenance of Generalized Association Rules Under Transaction Update and Taxonomy Evolution -- Prince: An Algorithm for Generating Rule Bases Without Closure Computations -- Text Processing and Classification -- Efficient Compression of Text Attributes of Data Warehouse Dimensions -- Effectiveness of Document Representation for Classification -- 2-PS Based Associative Text Classification -- Miscellaneous Applications -- Intrusion Detection via Analysis and Modelling of User Commands -- Dynamic Schema Navigation Using Formal Concept Analysis -- Security and Privacy Issues -- FMC: An Approach for Privacy Preserving OLAP -- Information Driven Evaluation of Data Hiding Algorithms -- Patterns -- Essential Patterns: A Perfect Cover of Frequent Patterns -- Processing Sequential Patterns in Relational Databases -- Optimizing a Sequence of Frequent Pattern Queries -- A General Effective Framework for Monotony and Tough Constraint Based Sequential Pattern Mining -- Cluster and Classification I -- Hiding Classification Rules for Data Sharing with Privacy Preservation -- Clustering-Based Histograms for Multi-dimensional Data -- Weighted K-Means for Density-Biased Clustering -- Cluster and Classification II -- A New Approach for Cluster Detection for Large Datasets with High Dimensionality -- Gene Expression Biclustering Using Random Walk Strategies -- Spectral Kernels for Classification -- Data Warehousing and Knowledge Discovery: A Chronological View of Research Challenges. |
| Record Nr. | UNISA-996465557403316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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The Elements of Big Data Value [[electronic resource] ] : Foundations of the Research and Innovation Ecosystem
| The Elements of Big Data Value [[electronic resource] ] : Foundations of the Research and Innovation Ecosystem |
| Autore | Curry Edward |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (412 p.) |
| Altri autori (Persone) |
MetzgerAndreas
ZillnerSonja PazzagliaJean-Christophe García RoblesAna |
| Soggetto topico |
Information retrieval
Business & management Research & development management Information technology industries Databases |
| Soggetto non controllato |
Information Storage and Retrieval
Business and Management, general Innovation/Technology Management The Computer Industry Big Data Innovation and Technology Management Technology Commercialization Digital Transformation Innovation Spaces Data-Driven Innovation Data Analytics Technology Management Data Ecosystems Data Protection Big Data Business Models Open Access Information retrieval Data warehousing Business & Management Research & development management Industrial applications of scientific research & technological innovation Information technology industries Databases |
| ISBN | 3-030-68176-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464495403316 |
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Curry Edward
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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The Elements of Big Data Value : Foundations of the Research and Innovation Ecosystem
| The Elements of Big Data Value : Foundations of the Research and Innovation Ecosystem |
| Autore | Curry Edward |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (412 p.) |
| Altri autori (Persone) |
MetzgerAndreas
ZillnerSonja PazzagliaJean-Christophe García RoblesAna |
| Collana | Computer Science Series |
| Soggetto topico |
Information retrieval
Business & management Research & development management Information technology industries Databases |
| Soggetto non controllato |
Information Storage and Retrieval
Business and Management, general Innovation/Technology Management The Computer Industry Big Data Innovation and Technology Management Technology Commercialization Digital Transformation Innovation Spaces Data-Driven Innovation Data Analytics Technology Management Data Ecosystems Data Protection Big Data Business Models Open Access Information retrieval Data warehousing Business & Management Research & development management Industrial applications of scientific research & technological innovation Information technology industries Databases |
| ISBN | 3-030-68176-9 |
| Classificazione | BUS042000BUS070030BUS087000COM021000COM030000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Foreword -- Foreword -- Preface -- Acknowledgements -- Contents -- Editors and Contributors -- Part I: Ecosystem Elements of Big Data Value -- The European Big Data Value Ecosystem -- 1 Introduction -- 2 What Is Big Data Value? -- 3 Strategic Importance of Big Data Value -- 4 Developing a European Big Data Value Ecosystem -- 4.1 Challenges -- 4.2 A Call for Action -- 4.3 The Big Data Value PPP (BDV PPP) -- 4.4 Big Data Value Association -- 5 The Elements of Big Data Value -- 5.1 Ecosystem Elements of Big Data Value -- 5.2 Research and Innovation Elements of Big Data Value -- 5.3 Business, Policy and Societal Elements of Big Data Value -- 5.4 Emerging Elements of Big Data Value -- 6 Summary -- References -- Stakeholder Analysis of Data Ecosystems -- 1 Introduction -- 2 Stakeholder Analysis -- 3 Who Is a Stakeholder? -- 4 Methodology -- 4.1 Phase 1: Case Studies -- 4.2 Phase 2: Cross-Case Analysis -- 5 Sectoral Case Studies -- 6 Cross-Case Analysis -- 6.1 Technology Adoption Stage -- 6.2 Data Value Chain -- 6.3 Strategic Impact of IT -- 6.4 Stakeholder Characteristics -- 6.5 Stakeholder Influence -- 7 Summary -- References -- A Roadmap to Drive Adoption of Data Ecosystems -- 1 Introduction -- 2 Challenges for the Adoption of Big Data Value -- 3 Big Data Value Public-Private Partnership -- 3.1 The Big Data Value Ecosystem -- 4 Five Mechanism to Drive Adoption -- 4.1 European Innovation Spaces (i-Spaces) -- 4.2 Lighthouse Projects -- 4.3 Technical Projects -- 4.4 Platforms for Data Sharing -- 4.4.1 Industrial Data Platforms (IDP) -- 4.4.2 Personal Data Platforms (PDP) -- 4.5 Cooperation and Coordination Projects -- 5 Roadmap for Adoption of Big Data Value -- 6 European Data Value Ecosystem Development -- 7 Summary -- References -- Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far.
1 Introduction -- 2 The Big Data Value PPP -- 2.1 BDV PPP Vision and Objectives for European Big Data Value -- 2.2 Big Data Value Association (BDVA) -- 2.3 BDV PPP Objectives -- 2.4 BDV PPP Governance -- 2.5 BDV PPP Monitoring Framework -- 3 Main Activities and Achievements During 2018 -- 3.1 Mobilisation of Stakeholders, Outreach, Success Stories -- 4 Monitored Achievements and Impact of the PPP -- 4.1 Achievement of the Goals of the PPP -- 4.2 Progress Achieved on KPIs -- 4.2.1 Private Investments -- 4.2.2 Job Creation, New Skills and Job Profiles -- 4.2.3 Impact of the BDV PPP on SMEs -- 4.2.4 Innovations Emerging from Projects -- 4.2.5 Supporting Major Sectors and Major Domains with Big Data Technologies and Applications -- 4.2.6 Experimentation -- 4.2.7 SRIA Implementation and Update -- 4.2.8 Technical Projects -- 4.2.9 Macro-economic KPIs -- 4.2.10 Contributions to Environmental Challenges -- 4.2.11 Standardisation Activities with European Standardisation Bodies -- 5 Summary and Outlook -- References -- Part II: Research and Innovation Elements of Big Data Value -- Technical Research Priorities for Big Data -- 1 Introduction -- 2 Methodology -- 2.1 Technology State of the Art and Sector Analysis -- 2.2 Subject Matter Expert Interviews -- 2.3 Stakeholder Workshops -- 2.4 Requirement Consolidation -- 2.5 Community Survey -- 3 Research Priorities for Big Data Value -- 3.1 Priority `Data Management´ -- 3.1.1 Challenges -- 3.1.2 Outcomes -- 3.2 Priority `Data Processing Architectures´ -- 3.2.1 Challenges -- 3.2.2 Outcomes -- 3.3 Priority `Data Analytics´ -- 3.3.1 Challenges -- 3.3.2 Outcomes -- 3.4 Priority `Data Visualisation and User Interaction´ -- 3.4.1 Challenges -- 3.4.2 Outcomes -- 3.5 Priority `Data Protection´ -- 3.5.1 Challenges -- 3.5.2 Outcomes -- 4 Big Data Standardisation -- 5 Engineering and DevOps for Big Data -- 5.1 Challenges. 5.2 Outcomes -- 6 Illustrative Scenario in Healthcare -- 7 Summary -- References -- A Reference Model for Big Data Technologies -- 1 Introduction -- 2 Reference Model -- 2.1 Horizontal Concerns -- 2.1.1 Data Visualisation and User Interaction -- 2.1.2 Data Analytics -- 2.1.3 Data Processing Architectures -- 2.1.4 Data Protection -- 2.1.5 Data Management -- 2.1.6 Cloud and High-Performance Computing (HPC) -- 2.1.7 IoT, CPS, Edge and Fog Computing -- 2.2 Vertical Concerns -- 2.2.1 Big Data Types and Semantics -- 2.2.2 Standards -- 2.2.3 Communication and Connectivity -- 2.2.4 Cybersecurity -- 2.2.5 Engineering and DevOps for Building Big Data Value Systems -- 2.2.6 Marketplaces, Industrial Data Platforms and Personal Data Platforms (IDPs/PDPs), Ecosystems for Data Sharing and Innovat... -- 3 Transforming Transport Case Study -- 3.1 Data Analytics -- 3.2 Data Visualisation -- 3.3 Data Management -- 3.4 Assessing the Impact of Big Data Technologies -- 3.5 Use Case Conclusion -- 4 Summary -- References -- Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving T... -- 1 Introduction -- 1.1 Aim of the Chapter -- 1.2 Context -- 2 Challenges to Security and Privacy in Big Data -- 3 Current Trends and Solutions in Privacy-Preserving Technologies -- 3.1 Trend 1: User-Centred Data Protection -- 3.2 Trend 2: Automated Compliance and Tools for Transparency -- 3.3 Trend 3: Learning with Big Data in a Privacy-Friendly and Confidential Way -- 3.4 Future Direction for Policy and Technology Development: Implementing the Old and Developing the New -- 4 Recommendations for Privacy-Preserving Technologies -- References -- A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence -- 1 Introduction -- 2 Innovation Ecosystems and Centres of Excellence. 2.1 What Are Centres of Excellence? -- 3 Methodology -- 4 Best Practice Framework for Big Data and Artificial Intelligence Centre of Excellence -- 4.1 Environment -- 4.1.1 Industry -- 4.1.2 Policy -- 4.1.3 Societal -- 4.2 Strategic Capabilities -- 4.2.1 Strategy -- 4.2.2 Governance -- 4.2.3 Structure -- 4.2.4 Funding -- 4.2.5 People -- 4.2.6 Culture -- 4.3 Operational Capabilities -- 4.4 Impact -- 4.4.1 Economic Impact -- 4.4.2 Scientific Impact -- 4.4.3 Societal Impact -- 4.4.4 Impact Measured Through KPIs -- 5 How to Use the Framework -- 5.1 Framework in Action -- 6 Critical Success Factors for Centres of Excellence -- 6.1 Challenges -- 6.2 Success Factors -- 6.3 Mechanisms to Address Challenges -- 6.4 Ideal Situation -- 7 Summary -- References -- Data Innovation Spaces -- 1 Introduction -- 2 Introduction to the European Data Innovation Spaces -- 3 Key Elements of an i-Space -- 4 Role of an i-Space and its Alignment with Other Initiatives -- 5 BDVA i-Spaces Certification Process -- 6 Impact of i-Spaces in Their Local Innovation Ecosystems -- 7 Cross-Border Collaboration: Towards a European Federation of i-Spaces -- 8 Success Stories -- 8.1 CeADAR: Ireland´s Centre for Applied Artificial Intelligence -- 8.2 CINECA -- 8.3 EGI -- 8.4 EURECAT/Big Data CoE Barcelona -- 8.5 ITAINNOVA/Aragon DIH -- 8.6 ITI/Data Cycle Hub -- 8.7 Know-Center -- 8.8 NCSR Demokritos/Attica Hub for the Economy of Data and Devices (ahedd) -- 8.9 RISE/ICE by RISE -- 8.10 Smart Data Innovation Lab (SDIL) -- 8.11 TeraLab -- 8.12 Universidad Politécnica de Madrid/Madrid´s i-Space for Sustainability/AIR4S DIH -- 9 Summary -- Reference -- Part III: Business, Policy, and Societal Elements of Big Data Value -- Big Data Value Creation by Example -- 1 Introduction -- 2 How Can Big Data Transform Everyday Mobility and Logistics?. 3 Digitalizing Forestry by Harnessing the Power of Big Data -- 4 GATE: First Big Data Centre of Excellence in Bulgaria -- 5 Beyond Privacy: Ethical and Societal Implications of Data Science -- 6 A Three-Year Journey to Insights and Investment -- 7 Scaling Up Data-Centric Start-Ups -- 8 Campaign Booster -- 9 AI Technology Meets Animal Welfare to Sustainably Feed the World -- 10 Creating the Next Generation of Smart Manufacturing with Federated Learning -- 11 Towards Open and Agile Big Data Analytics in Financial Sector -- 12 Electric Vehicles for Humans -- 13 Enabling 5G in Europe -- 14 Summary -- References -- Business Models and Ecosystem for Big Data -- 1 Introduction -- 2 Big Data Business Approaches -- 2.1 Optimisation and Improvements -- 2.2 Upgrading and Revaluation -- 2.3 Monetising -- 2.4 Breakthrough -- 3 Data-Driven Business Opportunities -- 4 Leveraging the Data Ecosystems -- 4.1 Data-Sharing Ecosystem -- 4.2 Data Innovation Ecosystems -- 4.3 Value Networks in a Business Ecosystem -- 5 Data-Driven Innovation Framework and Success Stories -- 5.1 The Data-Driven Innovation Framework -- 5.2 Examples of Success Stories -- 5.2.1 Selectionnist -- 5.2.2 Arable -- 6 Conclusion -- References -- Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework -- 1 Introduction -- 2 Data-Driven Innovation -- 2.1 What Are Business Opportunities? -- 2.2 Characteristics of Data-Driven Innovation -- 2.3 How to Screen Data-Driven Innovation? -- 3 The ``Making-of´´ the DDI Framework -- 3.1 State-of-the-Art Analysis -- 3.2 DDI Ontology Building -- 3.3 Data Collection and Coding -- 3.3.1 Selection Criteria -- 3.3.2 Sample Data Generation -- 3.3.3 Coding of Data -- 3.4 Data Analysis -- 4 Findings of the Empirical DDI Research Study -- 4.1 General Findings -- 4.2 Value Proposition -- 4.3 Data -- 4.4 Technology. 4.5 Network Strategies. |
| Record Nr. | UNINA-9910488709403321 |
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Curry Edward
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Evaluating information retrieval and access tasks : NTCIR's legacy of research impact / / edited by Tetsuya Sakai, Douglas W. Oard, Noriko Kando
| Evaluating information retrieval and access tasks : NTCIR's legacy of research impact / / edited by Tetsuya Sakai, Douglas W. Oard, Noriko Kando |
| Autore | Sakai Tetsuya |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (XIII, 219 pages 25 illustrations, 11 illustrations in color.) : digital, PDF file(s) |
| Disciplina | 025.04 |
| Collana | The Information Retrieval Series |
| Soggetto topico |
Information retrieval
Information Storage and Retrieval |
| Soggetto non controllato |
Information Storage and Retrieval
Evaluation Information Retrieval Multilingual Information Access NTCIR Test Collections Information Search Information Storage Artificial Intelligence Open Acces Information retrieval Data warehousing |
| ISBN | 981-15-5554-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Graded Relevance -- Chapter 2. Experiments on Cross-Language Information Retrieval using Comparable Corpora of Chinese, Japanese, and Korean Languages -- Chapter 3. Text Summarization Challenge -- Chapter 4. Challenges in Patent Information Retrieval -- Chapter 5. Multi-Modal Summarization -- Chapter 6. Opinion Analysis Corpora Across Languages -- Chapter 7. Patent Translation -- Chapter 8. Component-Based Evaluation for Question Answering -- Chapter 9. Temporal Information Access -- Chapter 10. SogouQ -- Chapter 11. Evaluation of Information Access with Smartphones -- Chapter 12. Mathematical Information Retrieval -- Chapter 13. Experiments in Lifelog Organisation and Retrieval at NTCIR -- Chapter 14. The Future of Information Retrieval Evaluation. |
| Record Nr. | UNISA-996464380203316 |
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Sakai Tetsuya
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Evaluating information retrieval and access tasks : NTCIR's legacy of research impact / / edited by Tetsuya Sakai, Douglas W. Oard, Noriko Kando
| Evaluating information retrieval and access tasks : NTCIR's legacy of research impact / / edited by Tetsuya Sakai, Douglas W. Oard, Noriko Kando |
| Autore | Sakai Tetsuya |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (XIII, 219 pages 25 illustrations, 11 illustrations in color.) : digital, PDF file(s) |
| Disciplina | 025.04 |
| Collana | The Information Retrieval Series |
| Soggetto topico |
Information retrieval
Information Storage and Retrieval |
| Soggetto non controllato |
Information Storage and Retrieval
Evaluation Information Retrieval Multilingual Information Access NTCIR Test Collections Information Search Information Storage Artificial Intelligence Open Acces Information retrieval Data warehousing |
| ISBN | 981-15-5554-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Graded Relevance -- Chapter 2. Experiments on Cross-Language Information Retrieval using Comparable Corpora of Chinese, Japanese, and Korean Languages -- Chapter 3. Text Summarization Challenge -- Chapter 4. Challenges in Patent Information Retrieval -- Chapter 5. Multi-Modal Summarization -- Chapter 6. Opinion Analysis Corpora Across Languages -- Chapter 7. Patent Translation -- Chapter 8. Component-Based Evaluation for Question Answering -- Chapter 9. Temporal Information Access -- Chapter 10. SogouQ -- Chapter 11. Evaluation of Information Access with Smartphones -- Chapter 12. Mathematical Information Retrieval -- Chapter 13. Experiments in Lifelog Organisation and Retrieval at NTCIR -- Chapter 14. The Future of Information Retrieval Evaluation. |
| Record Nr. | UNINA-9910418343803321 |
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Sakai Tetsuya
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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