Replenish Approach in Non Homogeneous Structured Dataset using Interpolation Techniques
V. Narayani1, S.Raj Kumar2

1Dr. V. Narayani, Associate Professor, Department of MCA, Karpagam College of Engineering, Coimbatore (Tamil Nadu), India.
2Dr. S. Rajkumar, Assistant Professor SG, Department of CSE, SNS College of Engineering, Coimbatore (Tamil Nadu), India.
Manuscript received on 11 March 2014 | Revised Manuscript received on 20 March 2014 | Manuscript Published on 30 March 2014 | PP: 68-71 | Volume-3 Issue-10, March 2014 | Retrieval Number: J15430331014/14©BEIESP
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Abstract: Replenish approach refers to the behavior of filling the fill the gaps in a table. Suppose that one has a table listing the population of some country in 1970, 1980, 1990 and 2000, and that one wanted to estimate the population in 1994.It lead us to implement the Numerical methods scxenario to solve this issue.The basic operation of linear interpolation between two values is so commonly used in computer graphics that it is sometimes called alerp in that field’s jargon. The term can be used as a verb or noun for the operation. e.g. “Bresenham’s algorithm lerps incrementally between the two endpoints of the line.”. The behaviors in Distributed Database environment are joining a relation, sharing resources, extraction on queries, etc. we aim to learn to predict the missed datum in distributed database. The connections in this environment are not homogenous. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. However, the connections in distributed database are often multi-dimensional. The heterogeneity presented in network connectivity can hinder the success of collective inference. Interpolation-based approach has been shown effective in addressing the heterogeneity of connections presented in distributed database system. The scale of these networks entails scalable learning of models for replenish prediction. This scheme is very sensitive to handle heterogeneity of distributed database system.In this paper we aim to predict the heterogeneity of two different environments by applying the interpolation schema which result the expected tuples. This method improves the performance of data extraction. Handling the heterogeneity of all distributed environments will be the future work.
Keywords: Data mining, DDBMS, Interpolation, Replenish, Replication.

Scope of the Article: Data Mining Methods, Techniques, and Tools