A premeditated cdm algorithm in cloud computing environment for fpm 2

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME
213
A PREMEDITATED CDM ALGORITHM IN CLOUD COMPUTING
ENVIRONMENT FOR FPM
Kuldeep Mishra1
M Tech scholar (CSE), SRGI, Jhansi, INDIA,
Ravi Rai Chaudhary2
Asst. Prof (CSE), SRGI, Jhansi, INDIA,
Dheresh Soni3
Assistant Professor (CSE), PCST, Bhopal, INDIA,
ABSTRACT
Cloud computing is a new concept with a broad definition. It has been acknowledged as one
of the prevailing models for providing IT capacities Cloud computing is a new network computing
paradigm based on IP architecture, and its potential lies in new business applications. The computing
paradigm that comes with cloud computing has incurred great concerns on the security of data,
especially the integrity and confidentiality of data, as cloud service providers may have complete
control on the computing infrastructure that underpins the services. The main task associated with
cloud computing is next generation data center transformation. In this paper we generalized the
formulation of data mining technique with cloud computing environment and generate the result. In
data mining we want to find useful patterns with different methodology. The main issue with data
mining techniques is that the space required for the item set and there operations are very huge. If we
combine data mining techniques with cloud computing environment, then we can rent the space from
the cloud providers on demand. This solution can solve the problem of huge space with better
application usage in low cost. We can apply data mining techniques without taking any consideration
of space. This paper basically survey and analyze the utility for solving the above situation.
Keywords: Cloud Computing, cloud storage, data mining, frequent pattern
I. INTRODUCTION
Cloud Computing [1,2] is a new business model. The term “Cloud computing” describes it as
a system platform or a kind of software application. First, a system platform means, based on real
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 4, July-August (2013), pp. 213-223
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214
time, it can dynamically proviso, configure, re-configure and de-proviso a system. In a cloud
computing platform, server is a physical server or a virtual server. High end cloud computing
generally includes other computation resources.
Cloud computing is a new concept with a broad definition. Cloud computing is a new
network computing paradigm based on IP architecture, and its potential lies in new business
applications. For the majority of operators and enterprises, the main task associated with cloud
computing is next generation data center transformation. "Computing" generally refers to computing
application; that is, any IT application in industry or in the market. Because network technologies are
being converged, all applications in information, communication, and video are integrated on a
unified platform. Likewise, computing in cloud computing refers to any integrated application. The
key characteristic of cloud computing is not "computing" but "cloud." It distributes the computing
tasks to the resource pool constituted of a large number of computers, so that a variety of application
systems can obtain computing power, storage space and a variety of software services on demand.
The novelty of the Cloud Computing is that it almost provides unlimited cheap storage and
computing power. This provides a platform for the storage and mining of mass data.
The role of data analytics increases in several request domains to cope with the big amount of
captured data. Cloud computing adopt virtualization, service-oriented architecture, autonomic
computing, and utility computing. Cloud computing is the delivery of computing as a service rather
than a product, whereby shared resources, software, and information are provided to computers and
other devices as a utility over a network. This will ensure cloud computing becomes more
widespread among enterprises, institutions, organizations, and operators. Cloud computing not only
provides traditional IT resource usage and application services, but also supports full resource usage
and application services such as IT, communications, video, mobile, and Internet of Things using a
converged network infrastructure. Cloud computing technologies include Key feature of unified
fabric, unified virtualization, and unified computing system.
Cloud computing has become one of the key considerations both in academic world and
industry. Cheap, apparently infinite computing resources that can be allocated approximately right
away and pay-as-you-go pricing schemes are some of the reasons for the success of cloud
computing. We discuss few aspects of cloud computing and also there area. We propose a novel
approach which is cloud computing mapping and management through class and object hierarchy. In
this approach we first design a cloud environment where we can analyze several object oriented
aspects based on some assumptions. Then we deduce message passing behavior through a backup
files based on the properties of object orient like class and object.
Association rule mining is an important research topic of data mining; its task is to find all
subsets of items which frequently occur, and the relationship between them. Association rule mining
has two main steps: the establishment of frequent item sets and the establishment of association
rules. Apriori algorithm [3] is the most classic and most widely used algorithm for mining frequent
item sets which generate Boolean association rules. The algorithm uses an iterative method called
layer search to generate (k + l) item sets from the k item sets. In this paper we describe a new
algorithm which provides the way for data mining or data mining association on cloud environment
so that we can achieve a better way to handle a large amount of data.
In this paper we discuss several technical issues related to security concern.We provide here
an overview of executing data mining services on grid. The rest of this paper is arranged as follows:
Section 2 introduces Cloud Computing; Section 3 describes about data mining; Section 4 shows the
Recent Scenario; Section 5 describes the proposed method. Section 6 describes Conclusion and
future prospect.

215
II. CLOUD COMPUTING
Cloud computing is the delivery of computing as a service rather than a product, whereby
shared resources, software, and information are provided to computers and other devices as
a utility (like the electricity grid) over a network (typically the Internet). A Cloud is a type of parallel
and distributed system consisting of a collection of interconnected and virtualized computers that are
dynamically provisioned and presented as one or more unified computing resources based on
service-level agreements established through negotiation between the service provider and
consumers. The increased degree of connectivity and the increasing amount of data has led many
providers and in particular data centers to employ larger infrastructures with dynamic load and
access balancing.
A cloud service has three distinct characteristics that differentiate it from traditional hosting.
1. It is sold on demand, typically by the minute or the hour. 2. It is elastic - a user can have as much
or as little of a service as they want at any given time 3. Service is fully managed by the provider (the
consumer needs nothing but a personal computer and Internet access). There are several reasons to
adopt cloud computing like cost, scalability, business agility, and disaster recovery. Cloud
computing is a model for enabling convenient, on-demand network access to a shared pool of
configurable computing resources that can be rapidly provisioned and released with minimal
management effort or service provider interaction.
This cloud model promotes availability and is composed of:
1.Four deployment models:
1.1 Private cloud
1.2 Community cloud
1.3 Public cloud
1.4 Hybrid cloud
2.Three service models:
2.1 Cloud Software as a Service (SaaS)
2.2 Cloud Platform as a Service (PaaS)
2.3 Cloud Infrastructure as a Service (IaaS)
3.Five essential characteristics:
3.1 On-demand self-service
3.2 Broad network access
3.3 Resource pooling
3.4 Rapid elasticity
3.5 Measured Service
4.Key enabling technologies include:
4.1 Fast wide-area networks
4.2 Powerful, inexpensive server computers
4.3 High-performance virtualization commodity hardware

216
Fig.1 Cloud Computing
Fig. 2 Architecture of Cloud Computing
In general, a public (external) cloud is an environment that exists outside a company’s
firewall. It can be a service offered by a third-party vendor. It could also be referred to as a shared or
multi-tenanted, virtualized infrastructure managed by means of a self-service portal.
A private (Internal) cloud reproduces the delivery models of a public cloud and does so
behind a firewall for the exclusive benefit of an organization and its customers. The self-service
administration interface is still in place while the IT infrastructure resources being collected are
internal. In a hybrid cloud environment, external services are leveraged to extend or supplement an
internal cloud.

217
III. DATA MINING TECHNIQUES
Here are several major data mining techniques have been developed and used in data mining
projects recently including association, classification, clustering, prediction and sequential patterns.
We will briefly examine those data mining techniques with example to have a good overview of
them.
Association
Association is one of the best known data mining technique. In association, a pattern is
discovered based on a relationship of a particular item on other items in the same transaction. For
example, the association technique is used in market basket analysis to identify what products that
customers frequently purchase together. Based on this data businesses can have corresponding
marketing campaign to sell more products to make more profit.
Classification
Classification is a classic data mining technique based on machine learning. Basically
classification is used to classify each item in a set of data into one of predefined set of classes or
groups. Classification method makes use of mathematical techniques such as decision trees, linear
programming, neural network and statistics. In classification, we make the software that can learn
how to classify the data items into groups. For example, we can apply classification in application
that “given all past records of employees who left the company, predict which current employees are
probably to leave in the future.” In this case, we divide the employee’s records into two groups that
are “leave” and “stay”. And then we can ask our data mining software to classify the employees into
each group.
Clustering
Clustering is a data mining technique that makes meaningful or useful cluster of objects that
have similar characteristic using automatic technique. Different from classification, clustering
technique also defines the classes and put objects in them, while in classification objects are assigned
into predefined classes. To make the concept clearer, we can take library as an example. In a library,
books have a wide range of topics available. The challenge is how to keep those books in a way that
readers can take several books in a specific topic without hassle. By using clustering technique, we
can keep books that have some kind of similarities in one cluster or one shelf and label it with a
meaningful name. If readers want to grab books in a topic, he or she would only go to that shelf
instead of looking the whole in the whole library.
Prediction
The prediction as it name implied is one of a data mining techniques that discovers
relationship between independent variables and relationship between dependent and independent
variables. For instance, prediction analysis technique can be used in sale to predict profit for the
future if we consider sale is an independent variable, profit could be a dependent variable. Then
based on the historical sale and profit data, we can draw a fitted regression curve that is used for
profit prediction.
Sequential Patterns
Sequential patterns analysis in one of data mining technique that seeks to discover similar
patterns in data transaction over a business period. The uncover patterns are used for further business
analysis to recognize relationships among data.

218
IV. RECENT SCENARIO
In 2010, Kawuu W.Lin et al. [4] proposed a set of strategies for many-task frequent pattern
mining. Through empirical evaluations on various simulation conditions, the proposed strategies
deliver excellent performance in terms of execution time.
In 2010, Yang Lai et al. [5] proposed a data mining framework on Hadoop using the Java
Persistence API (JPA) and MySQL Cluster. The framework is elaborated in the implementation of a
decision tree algorithm on Hadoop. We compare the data indexing algorithm with Hadoop MapFile
indexing, which performs a binary search, in a modest cloud environment. The results show the
algorithm is more efficient than naïve MapFile indexing. They compare the JDBC and JPA
implementations of the data mining framework. The performance shows the framework is efficient
for data mining on Hadoop.
In 2010, Jiabin Deng et al. [6] propose about the use of Power-law Distributions and
Improved Cubic Spline Interpolation for multi-perspective analysis of shareware download
frequency. The tasks include data mining the usage patterns and to build a mathematical model.
Through analysis and checks, in accordance with changes to usage requirements, our proposed
methods will intelligently adjust the data redundancy of cloud storage. Thus, storage resources are
fine tuned and storage efficiency is greatly enhanced.
In 2011, Lingjuan Li et al. [7] proposed a strategy of mining association rules in cloud
computing environment is focused on. Firstly, cloud computing, Hadoop, MapReduce programming
model, Apriori algorithm and parallel association rule mining algorithm are introduced. Then, a
parallel association rule mining strategy adapting to the cloud computing environment is designed. It
includes data set division method, data set allocation method, improved Apriori algorithm, and the
implementation procedure of the improved Apriori algorithm on MapReduce. Finally, the Hadoop
platform is built and the experiment for testing performance of the strategy as well as the improved
algorithm has been done.
In 2011, T.R. Gopalakrishnan Nair et al. [8] presents a specific method of implementing k-
means approach for data mining in such scenarios. In this approach data is geographically distributed
in multiple regions formed under several virtual machines. The results show that hierarchical virtual
k-means approach is an efficient mining scheme for cloud databases.
In 2011, Lingjuan Li et al. [9] Focus on the strategy of mining association rules in cloud
computing environment. Firstly, cloud computing, Hadoop, Map Reduce programming model,
Apriori algorithm and parallel association rule mining algorithm are introduced. Then, a parallel
association rule mining strategy adapting to the cloud computing environment is designed. It
includes data set division method, data set allocation method, improved Apriori algorithm, and the
implementation procedure of the improved Apriori algorithm on Map Reduce. Finally, the Hadoop
platform is built and the experiment for testing performance of the strategy as well as the improved
algorithm has been done.
In 2011, Fabrizio Marozzo et al. [10] present a Data Mining Cloud App framework that
supports the execution of parameter sweeping data mining applications on a Cloud. The framework
has been implemented using the Windows Azure platform, and evaluated through a set of parameter
sweeping clustering and classification applications. The experimental results demonstrate the
effectiveness of the proposed framework, as well as the scalability that can be achieved through the
parallel execution of parameter sweeping applications on a pool of virtual servers.
V. PROPOSED ALGORITHM (CDM)
In this section we describe our proposed methodology that is cloud data mining (CDM). Our
approach is easily understandable with the help of the flowchart [Fig. 3].

219
By the below flowchart we can easily understand the whole phenomena. In our approach we
can enter in the cloud or non cloud environment. Then we can perform data mining technique to find
frequent patterns and relevant associations. After the completion of data mining technique we can
deduce the cost in cloud and non cloud environment. Our Proposed algorithm is shown below:
Algorithm: CDM
1. Create a new database acting as non cloud environment
2. Again create a new database acting as a cloud environment.
3. We can add file from non clod or from the main database to the cloud environment when applying
for the operation.
4. We can enter in the data mining environment either through the cloud environment or from the
non cloud environment.
5. Read the database.
6. While (object.read()!=-1)
{
[Start Reading]
}
[compute the occurrences]
For i=1 to n iterations do
{
Itemset[i]=count;
Count++;
}
7. Make different associations based on the conditions.
8. Enter the minimum support.
[Enter the minimum support]
If(min-sup= = msdb)
{
Prune(db, key)
}
Else
{
[Empty]
}
Prune(db, key)
enter the min-sup
For i=1 to n do
If(count[i]>min-sup)
List=itemset[i];
Else
Remove from the list
9. Add the final result
10. Finish

220
Fig 3 Flowchart of CDM
VI. RESULT
When cloud computing introduced usage of cloud and its application is not so high. As the
time passed and people comes to know the advantage of this technology the usage increases
gradually and lot of application were also introduced in the cloud environment. Fig 4 Graph 1 shows
the result and scenario of increasing application of different category in cloud environment which
make cloud and its application easy for the users. The application which is developed in this
dissertation when merged with cloud environment it also increases the application category in cloud
environment. Fig. 5 Graph 2 shows the result in term of cost, application, and space in cloud
environment. When the user need to develop some application he has to purchases all his required
items like application software (required), storage, platform and database etc. This increases the cost
but if the user use cloud environment and take services (IAAS, PAAS, SAAS) from cloud service
provider the cost get reduced for the same application. Graph 2 shows that with the use of cloud user
can use application in low cost, better on demand application usage and full storage space provide by
cloud provider. Low cost means that user has to pay for the application which he use as per
agreement, Application usage get better using this application means, generally 50 percent of cloud
is need by provider from where he provide its service and remaining 50 percent is used by user. It is
better in term of performance. In this dissertation we had also compared the cost in the cloud and non
cloud environment considering only platform as a service as the role module in cloud computing.

221
0
10
20
30
40
50
60
70
80
20
40
60
80
Application
Appliaction
Fig 4 Graph 1
We took the calculation of cloud computing in the following manner:
Metric (on rent)
No. of
resource
Cost in Rs per month
Application Software 1 500 Rs per month
API for Application 1 100 Rs per month
Query (Load) for
application
1 100 Rs per month
Storage 1GB 100 Rs per month
Table 4 Graph 1”
The final output is deduce by the below formula
Cloud Cost = (Application Cost + API + Query Load + Storage) * month
In the non cloud environment we consider the computation in the following manner:
Metric No. of resource Cost
Application Software 1 10 thousand Rs.
Database (Oracle) 1 20 thousand Rs.
Database
(MS SQL Server)
1 20 thousand Rs.
Storage 100 GB 1 thousand Rs.
“Table 5 Graph 2”

222
The final output is deduce by the below formula
Non Cloud Cost = (Application Software + Storage+ Database) * (man * average salary) *
month
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Space
Application
Cost
“Fig. 5 Graph 2”
VII. CONCLUSION AND FUTURE PROSPECT
In this paper we want to generalize the formulation of data mining techniques with cloud
computing environment. In data mining we want to find useful patterns with different methodology.
The main issue with data mining techniques is that the space required for the item set and there
operations are very huge. If we combine data mining techniques with cloud computing environment,
then we can rent the space from the cloud providers on demand. This solution can solve the problem
of huge space and we can apply data mining techniques without taking any consideration of space.
This paper basically survey and analyze the utility for solving the above situation.
In future we concentrate on the real time scenario with their implementation and stress can be also
given over profit maximization using data mining.
VIII. REFERENCES
[1] A Weiss. “Computing in Clouds”, ACM Networker, 11(4):18-25,Dec.2007.
[2] R Buyya, CS Yeo, S Venugopal, Market-Oriented Cloud Computing:Vision, Hype, and
Reality for Delivering IT Services as Computing Utilities. Proceedings of the 2008 10th
IEEE International Conference on High Performance Computing and Communications.
Vol.00, pp, 5-13, 2008.
[3] Shao Feng jing, Yu Zhong qing. Principle and Algorithm of Data Mining [M]. Beijing: China
WaterPower Press, 2003. 2, 126-170.
[4] KawuuW.Lin , Yu-ChinLuo ,” Efficient Strategies for Many-task Frequent Pattern Mining in
Cloud Computing Environments”,2010 IEEE.
[5] Yang Lai , Shi ZhongZhi ,” An Efficient Data Mining Framework on Hadoop using Java
Persistence API” , 2010 10th IEEE International Conference on Computer and Information
Technology (CIT 2010).

223
[6] Jiabin Deng, JuanLi Hu, Anthony Chak Ming LIU, Juebo Wu, “Research and Application of
Cloud Storage”,2010 IEEE.
[7] Lingjuan Li , Min Zhang , “The Strategy of Mining Association Rule Based on Cloud
Computing”, 2011 IEEE.
[8] T.R. Gopalakrishnan Nair, K.Lakshmi Madhuri , “DATA MINING USING
HIERARCHICAL VIRTUAL K-MEANS APPROACH INTEGRATING DATA
FRAGMENTS IN CLOUD COMPUTING ENVIRONMENT”,2011 IEEE.
[9] Lingjuan Li, Min Zhang, “The Strategy of Mining Association Rule Based on Cloud
Computing”, 2011 International Conference on Business Computing and Global
Informatization.
[10] Fabrizio Marozzo , Domenico Talia , Paolo Trunfio , “A Cloud Framework for Parameter
Sweeping Data Mining Applications”, 2011 Third IEEE International Conference on Coud
Computing Technology and Science.
[11] http://searchcloudcomputing.techtarget.com/ definition /cloud-computing
[12] http://www.makeuseof.com/tag/cloud-computing-work- technology-explained/
[13] C. Bohm, S. Berchtold, H. P. Kriegel, and U. Michel, "Multidimensional index structures in
relational databases," in 1st International Conference on Data Warehousing and Knowledge
Discovery (DaWak 99), Florence, Italy, 1999, pp.51-70.
[14] J. Dean, S. Ghemawat, and Usenix, "MapReduce: Simplified data processing on large
clusters," in 6th Symposium on Operating Systems Design and Implementation (OSDI 04),
San Francisco, CA, 2004, pp. 137-149.
[15] J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns Without Candidate Generation. Proc. of
ACM Int. Conf. on Management of Data (SIGMOD), 2000, pp. 1-12.
[16] Gurudatt Kulkarni, Jayant Gambhir and Amruta Dongare, “Security in Cloud Computing”,
International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1,
2012, pp. 258 - 265, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[17] Rohini G.Khalkar and Prof. Dr. S.H.Patil, “Data Integrity Proof Techniques in Cloud
Storage”, International Journal of Computer Engineering & Technology (IJCET), Volume 4,
Issue 2, 2013, pp. 454 - 458, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[18] Abhishek Pandey, R.M.Tugnayat and A.K.Tiwari, “Data Security Framework for Cloud
Computing Networks”, International Journal of Computer Engineering & Technology
(IJCET), Volume 4, Issue 1, 2013, pp. 178 - 181, ISSN Print: 0976 – 6367, ISSN Online:
0976 – 6375.

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