Big Data : Techniques and Technologies in Geoinformatics / edited by Hassan A. Karimi.

Format:

Book

Contributor:

Karimi, Hassan A., editor.

Language:

English

Subjects (All):

Geography--Data processing.

Geography.

Big data.

Geographic information systems.

Geospatial data.

High performance computing.

Physical Description:

1 online resource (410 pages)

Edition:

Second edition.

Place of Publication:

Boca Raton, FL : CRC Press, [2025]

Summary:

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.

Contents:

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

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781040090268

1040090265

9781040090251

1040090257

9781003406969

1003406963

OCLC:

1428450835