Topics
 Problem Statement
 Define the problem
 Significance in context of the course
 Key Concepts
 Cloud Computing
 Spatial Cloud Computing
 Major Contributions of the paper
 Most significant - Why
 Preserve and Revise
Problem Statement
 Use of Cloud Computing to support the intensities of
geospatial sciences
 Reason for need of platform like Cloud Computing
 What is Cloud Computing?
 Spatial Cloud Computing (SCC)
 SCC Scenarios/Examples
 Opportunities & Challenges
 Cloud computing has been one of the most advancing
technologies recently. Utilizing it in the context of
Geo-Spatial sciences can prove to be very useful.
Cloud Computing
 Advancement of Distributed Computing
 Provides ‘computer as a service’ for end users
 In ‘pay-as-you-go’ model
 Model:
 Enables convenient, on-demand network access to a
shared pool of configurable computing resources

Ex: networks, servers, storage, applications and services
 Resources can be rapidly provisioned and released


With minimal management effort
Or with Service provider interaction
Services for Cloud Computing
 Cloud Computing is provided through 4 services
 Infrastructure as a Service (IaaS) – Amazon EC2
 Platform as a Service (PaaS) – MS Azure, Google Apps
 Software as a Service (SaaS) – Salesforce.com
 Data as a Service (DaaS)
For Geospatial Sciences
 Hadoop & Map Reduce can also be used
Uses of Cloud Services
 Earth Observation (EO) Data Access:
 DaaS is used for fast, secure access & utilization of EO data
 DaaS also provides the needed Storage & Processing needs
 Model:
 IaaS gives full control of computing instances
 But has network bottlenecks
 Cloud computing can be used in complement to solve computing
intensive problems
 Knowledge & Decision Support:
 Used by domain experts, managers or public
 SaaS provides good support
 Social Impact & Feedback:
 SaaS such as Facebook & email can be best utilized
Characteristics of Cloud Computing
 5 characteristics that distinguish Cloud Computing from
other distributed computing paradigms
 On-Demand Self Service
 For customers as needed automatically
 Broad Network Access
 For different types of network terminals
 Resource Pooling
 For consolidation of diff. types of Computing resources
 Rapid Elasticity
 For rapidly and elastically provisioning, allocating, and releasing
computing resources
 Measured Service
 To support pay-as-you-go approach
Spatial Cloud Computing
 Operation
of geospatial
computing environments
 Cloud computing
applications on
cloud
 Helps geospatial sciences
 Can be optimized with Spatiotemporal principles

Best utilize available distributed computing resources
 Geospatial Science Problems
 Have intensive Spatiotemporal constraints & Principles
 Best enabled if we consider general spatiotemporal rules
for geospatial domains
Spatial Cloud Computing Framework
SCC Scenarios
 4 scenarios given for 4 intensity problems. An
Example in the PPT.
 Data Intensity Scenario:
 Data Intensity issues in Geospatial sciences
characterized by 3 aspects



Multi-Dimensional
Massiveness
Globally distributed-organizations with data holdings are
distributed over entire earth
 Large volumes of data transferred
 Over fast computer networks
 Or collocated with processing to minimize transmitting
 Data Intensity scenario solution:
 Developing DaaS




Distributed inventory and portal based on SCC
To enable discoverability, accessibility & utilizability of
geospatial data
Stores millions to billions of metadata entries
 With data locations & performance awareness
Developed & Tested based on Microsoft Azure, Amazon EC2
& NASA Cloud Services
Opportunities & Challenges
 The grand challenges along 4 intensity problems can
be solved by latest advancements in cloud computing
 Opportunities:
 Spatiotemporal principle mining & extracting
 Important digital earth & complex geospatial science
and applications
 Supporting the SCC characteristics
 Security
 Citizen and Social Science
Major Contributions & Significant
 Categorization of grand challenges of Geospatial
Sciences in 21st century
 Good Explanations of Cloud Computing and Spatial
Cloud Computing with examples
 Insight with examples into how cloud computing can
solve 4 intensity problems


Most Significant
Looks ahead to see possible solutions for intensity problems
Preserve & Revise
 Revise
 Whole paper along recent advancements in cloud
computing
 Examples of SCC scenarios
 Preserve
 Initial different kinds of intensity definitions
 Cloud Computing & SCC key concepts
THANK YOU