| Description |
1 online resource (355 p.) |
| Series |
Special Publications |
|
Special Publications
|
| Contents |
Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Preface -- Chapter 1 An Introduction to Big Data Analytics -- 1.1 Overview -- 1.1.1 What Differentiates Spatial Big Data -- 1.2 Definitions -- 1.3 Example Problems -- 1.3.1 Agriculture -- 1.3.2 Commerce -- 1.3.3 Connected Cars -- 1.3.4 Environment -- 1.3.5 Financial Services -- 1.3.6 Government Agencies -- 1.3.7 Health Care -- 1.3.8 Marketing -- 1.3.9 Mining -- 1.3.10 Petroleum -- 1.3.11 Retail -- 1.3.12 Telecommunications -- 1.3.13 Transportation -- 1.3.14 Utilities -- 1.4 Big Data Analysis Concepts |
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1.4.1 Summarizing Data -- 1.4.2 Identify Locations -- 1.4.3 Pattern Analysis -- 1.4.4 Cluster Analysis -- 1.4.5 Proximity Analysis -- 1.4.6 Predictive Modeling -- 1.5 Technology and Tools -- 1.5.1 Available Tools -- 1.6 Challenges -- 1.7 Summary -- References -- Part I Big Data Analytics Architecture -- Chapter 2 Introduction to Big Data Analytics Architecture -- References -- Chapter 3 Scaling Big Earth Science Data Systems Via Cloud Computing -- 3.1 Introduction -- 3.2 Key Concepts of Science Data Systems (SDSes) -- 3.3 Increasing Data Processing, Volumes, and Rates -- 3.3.1 Historical Example |
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3.3.2 Example of On-Premises SDS -- 3.3.3 Exceeding On-Premise Capacities -- 3.4 Cloud Concepts for SDSes -- 3.4.1 IaaS (Infrastructure as a Service) -- 3.4.2 PaaS (Platform as a Service) -- 3.4.3 SaaS (Software as a Service) -- 3.4.4 XaaS (Everything as a Service) -- 3.5 Architecture Components of Cloud-Based SDS -- 3.5.1 Algorithm Development Environment -- 3.5.2 Processing Algorithm Catalog -- 3.5.3 Resource Management -- 3.5.4 Processing Orchestration/Workflow Management -- 3.5.5 Compute Services -- 3.5.6 Data Catalog -- 3.5.7 Data Storage Services |
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3.5.8 Common Services for Logging, Metrics, Events, and Analytics -- 3.5.9 Integrating Science Data Processing and Algorithm Development, Software Catalog, and Analysis -- 3.6 Considerations for Multi-cloud and Hybrid SDS -- 3.6.1 Collocation of GDS, SDS, and DAAC -- 3.6.2 All-In and Lock-In -- 3.7 Cloud Economics -- 3.8 Large-Scaling Considerations -- 3.8.1 Metrics for Anomalies -- 3.8.2 Thundering Herd -- 3.8.3 Higher SLA -- 3.8.4 Watchdogs -- 3.9 Example of Cloud SDSes -- 3.9.1 SMAP in the Cloud -- 3.9.2 NISAR SDS -- 3.10 Conclusion -- 3.10 References |
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Chapter 4 NOAA Open Data Dissemination (Formerly NOAA Big Data Project/Program) -- 4.1 Obstacles to the Public's Use of NOAA Environmental Data -- 4.2 Public Access of NOAA Data Creates Challenges for the Agency -- 4.3 The Vision for NOAA's "Oddball" Approach to Big Data -- 4.4 A NOAA Cooperative Institute Data Broker provides Research and Operational Agility -- 4.5 Public-Private Partnerships Provide the Pipeline -- 4.6 BDP Exceeds Expectations and Evolves into Enterprise Operations -- 4.7 Engaging Users in the Cloud -- 4.7.1 Early Insights From User Engagement |
| Notes |
Description based upon print version of record |
|
4.7.2 Data Analytics and Metrics Informing User Engagement |
| Form |
Electronic book
|
| Author |
Vance, Tiffany C
|
|
Lynnes, Christopher
|
| ISBN |
9781119467564 |
|
111946756X |
|
1119467551 |
|
9781119467557 |
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