Informatics Publishing Limited

S. Brintha Rajakumari ¹, C. Nalini ¹

Affiliations
1 Department of CSE, B. I. S. T. (Bharath University), Chennai, IN

Abstract

Data storage space is virtually infinite in cloud computing. To store data in the cloud requires more cost because it is a 'pay and use model' and also the resources are based on budget constraints. Normally, question performance is a very important concept in database management system. To improve that introduces novel approach which will reduce the cost of data storage within the cloud. This novel approach converts the given aggregated data into horizontal layout form, which will reduce the size of the data to store in the cloud. This paper presents the effective cost model for data storage with horizontal layout within the cloud.

Keywords

Cloud Computing, Cost Model, Horizontal Layout

Full Text

   

S. Brintha Rajakumari ¹, C. Nalini ¹

Affiliations
1 Department of CSE, B. I. S. T. (Bharath University), Chennai, IN

Abstract

To prepare the data set from relational database management system for data mining is very difficult and time consuming task. These prepared data can be used as input in data mining analysis. But traditional structured query language aggregate function returns the records in one column per aggregated group. This paper presents the horizontal representation of data used for dataset preparation in data mining analysis and reduce memory space when evaluated with the cancer dataset.

Keywords

Aggregation, Data Mining

Full Text

   

S. Ezhilmathi Sonia ¹, S. Brintha Rajakumar ¹, C. Nalini ¹

Affiliations
1 Department of CSE, B. I. S. T. (Bharath University), Chennai, TN, IN

Abstract

The data mining process to identify churners has concern with size of the dataset. This paper analyzes the telecom customer complaints and call quality datasets using Mapreduce to predict the customer churn. HDFS and Mapreduce make it possible to mine larger data sets without the constraints of the data size.

Keywords

Hadoop MapReduce, Telecommunication, Churn Analysis, Data Mining.

Full Text