Cloud computing QUESTION SET

 Cloud computing QUESTION SET 1-CLICK

 Cloud computing QUESTION SET 2- CLICK

 Cloud computing QUESTION SET 3- CLICK

 Cloud computing QUESTION SET 4-CLICK

 Cloud computing QUESTION SET 5- CLICK

 Cloud computing QUESTION SET 6- CLICK

 Cloud computing QUESTION SET 7-

CASE STUDIE - CLOUD

 Aneka

CometCloud

Autonomic cloud bridging

GOOGLE APP ENGINE

                      Google App Engine integrates the following tools:

                     1. Python runtime. 

                     2. Java runtime.

                    3. Software Development Kit (SDK): Enables developers to write application code. 

                    4. Web-based Administration Console: Helps developers manage their applications. 

                    5. A Datastore: A software layer that stores a web application’s data. 

AMAZON WEB SERVICES (AWS)

EUCALYPTUS

MS Azure,Azure Cache

Cloud computing QUESTION SET 5

 The Cloud Security Alliance

ELEMENTS OF CLOUD SECURITY MODEL

The Cloud Cube Model

Security Aspects

Platform-as-a-Service Security Issues

Infrastructure-as-a-Service Security Issues

Software-as-a-Service Security Issues

Disaster Recovery

BENEFITS OF TRUSTED CLOUD

CLOUD DATA ENCRYPTION

CLOUD DATA ENCRYPTION

SIX SURFACE ATTACKS

Cloud computing QUESTION SET 4

 Cloud Architecture

several approaches of cloud migration.

pay-as-you-go paradigm

server consolidation

Software-as-a-Service Security Issues

Cloud computing QUESTION SET 3

 Cloud Computing Model:

The NIST cloud computing model

On-demand self-service:

Resource pooling:

Public Cloud,Private Cloud,Community Cloud,Hybrid Cloud

■ Infrastructure-as-a-Service (IaaS)

 ■ Platform-as-a-Service (PaaS)

 ■ Software-as-a-Service (SaaS)

Security Management-as-a-Service

Identity Management-as-a-Service (IDaaS)

Storage-as-a-Service

Database-as-a-Service

Backup-as-a-Service (BaaS)

OPEN CLOUD SERVICES

5-4-3 Principles of Cloud computing

Cloud computing QUESTION SET 2

 1. THREE LAYERS OF COMPUTING

■ Infrastructure ■ Platform ■ Application

2.What is meant by ‘cloud’ in cloud computing?

3.Where do the clouds reside?

4.Cluster Computing:

5.Mainframe Architecture

6.Grid Computing

7. COMPARISON BETWEEN CLUSTER, GRID AND CLOUD COMPUTING

8. ROLE OF WEB SERVICE

 ■Simple Access Object Protocol (SOAP) based, or ■ Representational State Transfer (REST) compliant. 

9.Justify the ‘low initial investment’ philosophy of cloud computing.

Data mining Question set-6

 Partitioning Methods

 1.k-Means: A Centroid-Based Technique

2.Hierarchical Methods

3.a grid-based clustering

Data mining Question set-5

 1. Bayesian Belief Networks

2. Classification by Backpropagation

3. Classification Using Frequent Patterns

4. Lazy Learners (or Learning from Your Neighbors)

5. Semi-Supervised Classification

Data mining Question set-4

 1 Briefly outline the major steps of decision tree classification.

 2 Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning? 

3 Given a decision tree, you have the option of (a) converting the decision tree to rules and then pruning the resulting rules, or (b) pruning the decision tree and then converting the pruned tree to rules. What advantage does (a) have over (b)? 

4 It is important to calculate the worst-case computational complexity of the decision tree algorithm. Given data set, D, the number of attributes, n, and the number of training tuples, |D|, show that the computational cost of growing a tree is at most n × |D| × log(|D|).

5.Why is na¨ıve Bayesian classification called “na¨ıve”? Briefly outline the major ideas of na¨ıve Bayesian classification

6.Show that accuracy is a function of sensitivity and specificity

Data mining Question set-3

1. Define each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classification, regression, clustering,  outlier analysis. Give examples of each data mining functionality, using a real-life database that you are familiar with.

2.Cluster Analysis, Outlier Analysis

3.Machine Learning

        Supervised learning

        Unsupervised learning

        Semi-supervised learning

       Active learning 

4. Present an example where data mining is crucial to the success of a business. What data mining functionalities does this business need (e.g., think of the kinds of patterns that could be mined)? Can such patterns be generated alternatively by data query processing or simple statistical analysis? 

5 Explain the difference and similarity between discrimination and classification, between characterization and clustering, and between classification and regression. 

6 Based on your observations, describe another possible kind of knowledge that needs to be discovered by data mining methods but has not been listed in this chapter. Does it require a mining methodology that is quite different from those outlined in this chapter? 

7 Outliers are often discarded as noise. However, one person’s garbage could be another’s treasure. For example, exceptions in credit card transactions can help us detect the fraudulent use of credit cards. Using fraudulence detection as an example, propose two methods that can be used to detect outliers and discuss which one is more reliable. 

8 Describe three challenges to data mining regarding data mining methodology and user interaction issues. 

9 What are the major challenges of mining a huge amount of data (e.g., billions of tuples) in comparison with mining a small amount of data (e.g., data set of a few hundred tuple)? 

10 Outline the major research challenges of data mining in one specific application domain, such as stream/sensor data analysis, spatiotemporal data analysis, or bioinformatics.