Big Data : Techniques and Technologies in Geoinformatics

Big Data Techniques and Technologies in Geoinformatics

This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems. There are seven new chapters, and each of them focuses on a separate real-world problem to which deep learning is applied.

Detalles Bibliográficos
Otros Autores: Karimi, Hassan A., editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2025]
Edición:Second edition
Materias:
Geography > Data processing.
Big data.
Geographic information systems.
Geospatial data.
High performance computing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869130706719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • Editor
  • Contributors
  • Preface
  • Chapter 1: Distributed and Parallel Computing
  • 1.1 Introduction
  • 1.2 Distributed Computing
  • 1.2.1 Cluster Computing
  • 1.2.1.1 Architecture
  • 1.2.1.2 Data and Message Communication
  • 1.2.1.3 Task Management and Administration
  • 1.2.1.4 Example Geospatial Big Data Project on Cluster
  • 1.2.2 Grid Computing
  • 1.2.2.1 Architecture
  • 1.2.2.2 Types of Grid Architectures
  • 1.2.2.3 Topology
  • 1.2.2.4 Perspectives
  • 1.2.2.4.1 User
  • 1.2.2.4.2 Administrator
  • 1.2.2.4.3 Application Developer
  • 1.2.2.5 Example Geospatial Big Data Project on Grids
  • 1.2.3 Cloud Computing
  • 1.2.3.1 Taxonomies
  • 1.2.3.2 Cloud Service Models
  • 1.2.3.3 Cloud Deployment Models
  • 1.2.3.3.1 Cloud APIs
  • 1.2.3.3.2 Levels of Cloud APIs
  • 1.2.3.3.3 Categories of APIs
  • 1.2.3.4 Example Geospatial Big Data Project on Clouds
  • 1.3 Parallel Computing
  • 1.3.1 Classes of Parallel Computing
  • 1.3.2 Shared Memory Multiple Processing
  • 1.3.3 Distributed Memory Multiple Processing
  • 1.3.4 Hybrid Distributed Shared Memory
  • 1.3.5 Example Geospatial Big Data Project on Parallel Computers
  • 1.4 Supercomputing
  • 1.4.1 Supercomputing Worldwide
  • 1.4.2 Trend
  • 1.4.3 Future Supercomputer Research
  • 1.4.4 Example Geospatial Big Data Project on Supercomputers
  • 1.5 XSEDE: A Single Virtual System
  • 1.5.1 Resources
  • 1.5.2 Services
  • 1.5.3 Example Big Geospatial Data Project on XSEDE
  • 1.6 Choosing Appropriate Computing Environment
  • 1.7 Summary
  • References
  • Chapter 2: GEOSS Clearinghouse Integrating Geospatial Resources to Support the Global Earth Observation System of Systems
  • 2.1 Introduction
  • 2.2 Catalog and Clearinghouse Research Review
  • 2.2.1 Metadata Repository and Standardized Metadata.
  • 2.2.2 Catalog and Clearinghouse Based on Service-Oriented Architecture and Standard Services
  • 2.2.3 Semantic-Based Metadata Sharing and Data Discovery
  • 2.3 Technological Issues and Solutions
  • 2.3.1 Interoperability
  • 2.3.2 Provenance and Updating
  • 2.3.3 System Performance
  • 2.3.4 Timely Updating
  • 2.4 Design and Implementation of CLH
  • 2.4.1 Architecture
  • 2.4.2 Administration, User, and Group Management
  • 2.4.3 Harvesting
  • 2.4.4 Metadata Standards and Transformation
  • 2.4.5 User Interface and Programming APIs
  • 2.4.5.1 Search through Web Graphics User Interface
  • 2.4.5.2 Remote Search
  • 2.5 Usage and Operational Status
  • 2.5.1 System Operations
  • 2.5.2 System Metadata Status
  • 2.5.3 Usage
  • 2.6 Big Geospatial Data Challenges and Solutions
  • 2.6.1 Data and Database
  • 2.6.2 Distribution and Cloud Computing
  • 2.6.3 Searching Performance and Index
  • 2.7 Summary and Future Research
  • Acknowledgments
  • Notes
  • References
  • Chapter 3: Using a Cloud Computing Environment to Process Large 3D Spatial Datasets
  • 3.1 Introduction
  • 3.1.1 Big Spatial Data
  • 3.1.2 Need for Cloud Computing Environment
  • 3.2 Methodology
  • 3.2.1 Iowa LiDAR Database
  • 3.2.2 CLiPS Design and Implementation
  • 3.3 Results
  • 3.3.1 Application Example: DEM Generation Using Large LiDAR Datasets
  • 3.3.2 Heuristic Models Development
  • 3.4 Conclusions
  • Acknowledgment
  • References
  • Chapter 4: Building Open Environments to Meet Big Data Challenges in Earth Sciences
  • 4.1 Introduction
  • 4.2 Technology Foundation and Methodology
  • 4.2.1 Interoperability
  • 4.2.2 Serviceability
  • 4.2.3 Infrastructure
  • 4.3 Discussions
  • 4.4 Summary
  • References
  • Chapter 5: Developing Online Visualization and Analysis Services for NASA Satellite-Derived Global Precipitation Products during the Big Geospatial Data Era
  • 5.1 Introduction.
  • 5.2 Overview of Global Precipitation Products and Data Services
  • 5.2.1 TRMM Background
  • 5.2.2 TRMM Products
  • 5.2.3 TRMM Data Services
  • 5.2.4 Global Precipitation Measurement Mission
  • 5.3 Big Data Challenges and Solutions
  • 5.3.1 Big Data Challenges
  • 5.3.2 Solutions
  • 5.4 A Prototype
  • 5.4.1 Data
  • 5.4.2 System Description
  • 5.4.3 Examples
  • 5.5 Conclusions
  • Acknowledgment
  • References
  • Chapter 6: Algorithmic Design Considerations for Geospatial and/or Temporal Big Data
  • 6.1 Motivation
  • 6.1.1 Challenges
  • 6.1.1.1 Algorithmic Time Complexity
  • 6.1.1.2 Algorithmic Space Complexity
  • 6.2 Geospatial Big Data Algorithms: The State of the Art
  • 6.2.1 Volume Algorithms
  • 6.2.2 Velocity Algorithms
  • 6.2.3 Variety Algorithms
  • 6.3 Analysis of Classical Geospatial and Temporal Algorithms
  • 6.4 Approaches to Algorithmic Adaptation for Geospatial Big Data
  • 6.4.1 Divide and Conquer
  • 6.4.2 Subsampling
  • 6.4.3 Aggregation
  • 6.4.4 Filtering
  • 6.4.5 Online Algorithms
  • 6.4.6 Streaming Algorithms
  • 6.4.7 Iterative Algorithms
  • 6.4.8 Relaxation
  • 6.4.9 Convergent Algorithms
  • 6.4.10 Stochastic Algorithms
  • 6.4.11 Batch versus Online Algorithms
  • 6.4.12 Dimensionality Reduction
  • 6.4.13 Example
  • 6.5 Open Challenges
  • 6.6 Summary
  • References
  • Chapter 7: Machine Learning on Geospatial Big Data
  • 7.1 Motivation
  • 7.1.1 Supervised, Unsupervised, and Feature Learning
  • 7.1.1.1 Supervised Learning
  • 7.1.1.2 Unsupervised Learning
  • 7.1.1.3 Feature Learning
  • 7.1.2 Big Data Challenges
  • 7.1.3 Three Vs
  • 7.1.3.1 Volume
  • 7.1.3.2 Velocity
  • 7.1.3.3 Variety
  • 7.2 Geospatial Big Data Feature Learning
  • 7.2.1 Approaches to Big Data Feature Learning
  • 7.3 Reducing Dimensionality of Geospatial Big Data, Making Machine Learning Tractable
  • 7.3.1 Feature Construction
  • 7.3.1.1 Windowing in Raster Data.
  • 7.3.1.2 Windowing in Time Series Geographic Data
  • 7.3.1.3 Big Data Feature Construction
  • 7.3.2 Dimensionality Reduction
  • 7.3.2.1 Feature Selection
  • 7.3.2.2 Feature Extraction
  • 7.4 Algorithmic Approaches to Machine Learning of Geospatial Big Data
  • 7.4.1 Space Complexity
  • 7.4.1.1 Online Learning
  • 7.4.2 Time Complexity
  • 7.4.2.1 Online Learning
  • 7.4.2.2 Ensemble Learning
  • 7.5 Conclusions
  • Note
  • References
  • Chapter 8: Spatial Big Data: Case Studies on Volume, Velocity, and Variety
  • 8.1 Introduction
  • 8.2 What Is Spatial Big Data?
  • 8.3 Volume: Discovering Sub-Paths in Climate Data
  • 8.4 Velocity: Spatial Graph Outlier Detection in Traffic Data
  • 8.5 Variety in Data Types: Identifying Bike Corridors
  • 8.6 Variety in Output: Spatial Network Activity Summarization
  • 8.7 Summary
  • References
  • Chapter 9: Exploiting Big VGI to Improve Routing and Navigation Services
  • 9.1 Introduction
  • 9.2 What Is Big Data?
  • 9.3 VGI as Big Data
  • 9.4 Traditional Routing Services
  • 9.5 Routing Services using Big VGI/Crowdsourced Data
  • 9.5.1 Routing with Landmarks Extracted from Big VGI/Crowdsourced Data
  • 9.5.2 GPS Traces
  • 9.5.3 Social Media Reports
  • 9.6 Challenges for Exploiting Big VGI to Improve Routing Services
  • 9.6.1 Limitations of VGI and Crowdsourced Data
  • 9.6.2 Impact on the Development of Routing and Navigation Services
  • 9.6.2.1 Interoperability
  • 9.6.2.2 Finding the Right Data
  • 9.6.2.3 Analyzing and Interpreting Data
  • 9.6.3 Applicability of Big Data Solutions to Big VGI
  • 9.7 Summary
  • References
  • Chapter 10: Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts
  • 10.1 Introduction
  • 10.2 Background, Motivation, and Related Work
  • 10.3 Prototype System Architecture
  • 10.4 Experiments and Results
  • 10.4.1 Results of BC on Original Sequences.
  • 10.4.2 Results of Association Rule Mining on Original Sequences
  • 10.4.3 Results of the Proposed Approach
  • 10.5 Conclusion and Future Work
  • Notes
  • References
  • Chapter 11: Geoinformatics and Social Media: New Big Data Challenge
  • 11.1 Introduction: Social Media and Ambient Geographic Information
  • 11.2 Characteristics of Big Geosocial Data
  • 11.3 Geosocial Complexity
  • 11.4 Modeling and Analyzing Geosocial Multimedia: Heterogeneity and Integration
  • 11.5 Outlook: Grand Challenges and Opportunities for Big Geosocial Data
  • Notes
  • References
  • Chapter 12: Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern
  • 12.1 Introduction
  • 12.2 Trajectory Modeling
  • 12.2.1 TMC-Pattern
  • 12.2.1.1 Determining Meaningful Location
  • 12.2.2 Time Correlation
  • 12.2.3 Location Context Awareness
  • 12.2.4 Relevance Measures of a Region
  • 12.2.5 TMC-Pattern
  • 12.2.5.1 Determining Residence Mode of a Region
  • 12.2.6 Trajectory Extraction
  • 12.3 Trajectory Mining
  • 12.3.1 Frequent Locations from TMC-Pattern
  • 12.3.2 TMC-Pattern and Markov Chain for Prediction
  • 12.3.2.1 Markov Chains
  • 12.3.2.2 Markov Chain from TMC-Pattern
  • 12.3.2.3 Computation of Markov Chain Transition Probability
  • 12.3.2.4 Computation of Scores from TMC-Pattern
  • 12.4 Empirical Evaluations
  • 12.4.1 Experimental Dataset
  • 12.4.2 Evaluation of TMC-Pattern Extraction
  • 12.4.2.1 Single-User Data
  • 12.4.2.2 Multiuser Data
  • 12.4.3 Frequent Patterns
  • 12.4.4 Location Prediction
  • 12.5 Summary
  • References
  • Chapter 13: Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web
  • 13.1 Introduction
  • 13.2 Big Data Challenges on the Worldwide Sensor Web
  • 13.3 Worldwide Sensor Web Architecture
  • 13.4 GeoCENS Architecture
  • 13.4.1 OGC-Based Sensor Web servers.
  • 13.4.2 Decentralized Hybrid P2P Sensor Web Service Discovery.

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