The visualization of information contained in reports is an important aspect of human-computer interaction, for both the accuracy and the complexity of relationships between data must be preserved. A greater attention has been paid to individual report visualization through different types of standard graphs (Histograms, Pies, etc.). However, this kind of representation provides separate information items and gives no support to visualize their relationships which are extremely important for most decision processes. This paper presents a design methodology exploiting the visual language CoDe [1] based on a logic paradigm. CoDe allows to organize the visualization through the CoDe model which graphically represents relationships between information items and can be considered a conceptual map of the view. The proposed design methodology is composed of four phases: the CoDe Modelingand OLAP Operation pattern definition phases define the CoDe model and underlying metadata information, the OLAP Operation phase physically extracts data from a data warehouse and the Report Visualization phase generates the final visualization. Moreover,a case study on real data is provided. http://kaashivinfotech.com/ http://inplanttrainingchennai.com/ http://inplanttraining-in-chennai.com/ http://internshipinchennai.in/ http://inplant-training.org/ http://kernelmind.com/ http://inplanttraining-in-chennai.com/ http://inplanttrainingchennai.com/

1. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA
ENGINEERING, VOL. 26, NO. 3, MARCH 2014 “
CoDeModeling of Graph Composition for Data Warehouse Report
Visualization

2. Abstract
 The visualization of information contained in reports is an important
aspect of human-computer interaction, for both the accuracy and the
complexity of relationships between data must be preserved. A greater
attention has been paid to individual report visualization through
different types of standard graphs (Histograms, Pies, etc.).
 However, this kind of representation provides separate information
items and gives no support to visualize their relationships which are
extremely important for most decision processes.
 This paper presents a design methodology exploiting the visual
language CoDe [1] based on a logic paradigm. CoDe allows to organize
the visualization through the CoDe model which graphically represents
relationships between information items and can be considered a
conceptual map of the view.
 The proposed design methodology is composed of four phases: the
CoDeModelingand OLAP Operation pattern definition phases define
the CoDe model and underlying metadata information, the OLAP
Operation phase physically extracts data from a data warehouse and
the Report Visualization phase generates the final visualization.
Moreover,a case study on real data is provided.

3. Existing System
 In the existing system ,data mining seeks to discover knowledge
automatically in the form of statistical rules and patterns from
large databases.
 A data warehouse archives information gathered from multiple
sources, and stores it under a unified schema, at a single site.
 Where,
 __Important for large businesses that generate data from
multiple divisions, possibly at multiple sites
 __Data may also be purchased externally
 First order logic algorithm has been used in the existing
approach for integrating the data’s , aggregation algorithm for
mining has also been implemented in our approach.
 A report of data analysis will be generated from the repository of
data warehoused storage ,which tends to form an knowledge
cube data structure, where it denies the storage between
interrelated data’s which has been proposed in our further
approach. .

4. Proposed System
 Interactive analysis of data has been proposed ,by allowing
data to be summarized and viewed in different ways
(Pdf,CSV,Excel,etc) with negligible delay.
 Data that can be modeled as dimension attributes and
measure attributes are called multidimensional data where
it stores the entire data’s in the form of inter-related
storage in the data warehoused repository.
 Measure attributes
 measure some value
 can be aggregated upon
 e.g. the attribute number of the sales relation,stock,etc.
 Mediator based data integration algorithm has been
implemented for generalization & suppression of data’s in
the data warehoused paradigm ,whereas acute evasion
technique to handle bulk data has been deployed further in
our proposed approach.

5. System Requirements
 Hardware Requirements:
Platform : DOTNET (VS2010) , ASP.NET
Dot net framework 4.0
Database : SQL Server 2008 R2
 Software Requirements:
Processor : Core 2 duo
Speed : 2.2GHZ
RAM : 2GB
Hard Disk : 160GB

6. Architecture Diagram