CHAPTER 5 DESIGN AND DEVELOPMENT OF AN ...shodhganga.inflibnet.ac.in/bitstream/10603/33822/5/...p12 p13 0.024 p13 p14 -0.017 p11 p13 -0.1 Table 5.8 shows there is negative correlation

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CHAPTER 5

DESIGN AND DEVELOPMENT OF AN ALGORITHM FOR AN

ENHANCED MODEL IN WEB 3.0

5.1 INTRODUCTION

An algorithm has been designed for crafting a maximum spanning tree

model in web 3.0 for students, faculty and IT Professionals by imbibing the features

of high inter attribute correlation, Ordering and Fixation of root node enhanced from

the traditional maximum spanning tree algorithm and concept wise it has adopted

Backtracking. The enhancements evicted in this work are suited for the models which

require higher inter attribute correlation.

Discriminant analysis based modeling on the data set of web 3.0 for students

, faculty and IT professionals provides an insight into the high priority and low

priority clusters and also envisage the ordering of clusters for each of these categories.

Although Generic cluster is of least priority to the user groups Students, Faculty and

IT professionals it is impossible to construct a web 3.0 product without Generic

cluster . Alternatively it can be given least importance. When the correlation

coefficient is computed among all the clusters it has a positive correlation hence

included with least importance in the design and developmet model of web 3.0 for

students, faculty and IT professionals.

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Table 5.1 Correlation among Clusters in the data set for Students, Faculty

and IT Professionals

Cluster Generic Media Applications Platform Input Output

Generic 1 .709 .604 .461 .467 .543

Media .709 1 .544 .387 .463 .493

Applications .604 .544 1 .765 .499 .613

Platform .461 .387 .765 1 .397 .564

Input .467 .463 .499 .397 1 .407

Output .543 .493 .613 .564 .407 1

Table 5.1 depicts the correlation among the clusters in the data set

constructed in Students, Faculty and IT Professionals from which it is identified that

Generic cluster has positive correlation with all other clusters.

5.2 FRAMEWORK DESIGN FOR ENHANCED MAXIMUM SPANNING

TREE MODEL IN WEB 3.0

5.2.1 PARAMETERS FOR ENHANCEMENT

In the previous graphs when Kruskals, Prims and Borovkas are applied it

gave the same result by using different methodologies in different time complexity

but with the same cost. Dijktras algorithm is applied to obtain the shortest path to all

the other vertices from a single vertex by applying the concept of backtracking. When

Kruskals, Prims and Borovkas are applied it concentrates on an over all maximization

of the edge cost. The spanning tree model is considered based on the facts that all the

features of Web 3.0 are denoted as vertices after dimensionality reduction has to be


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included with an add on logic of high inter attribute correlation. The model in Web

3.0 for students , Faculty and IT professionals design and development product,

development has to concentrate on the following.

1. High inter attribute correlation

Inter attribute correlation must be maximized in the enhanced algorithm for

the design and development of Web 3.0 for students, faculty and IT professionals

since web 3 product design is based on the relationship between the parameters in

Web 3.0 to be incorporated . The features in Web 3.0 are correlated. Also the

inclusion of one feature is dependent on another feature being utilized in the product .

Since features incorporated in Web 3.0 products are interrelated and the high inter

attributes correlation should be maintained instead of concentrating on maximizing,

the over all cost , a new and enhanced algorithm has to be devised. Initially the top

most parameter adaptable for a category is identified followed by the next parameter

which is highly correlated with this parameter is to be identified as it will be the next

highly sought parameter in the Web 3.0 product for a particular category.

If the existing spanning tree is used it will concentrate on overall cost

maximization and hence the inter attribute correlation is not considered and inter

attribute correlation is weak for many parameter pairs.

2. Fixation of root node

The Web 3.0 products for students , faculty and IT professionals have a

query in identifying the feature to be incorporated initially in specific for the category

, so that the product can be developed with more importance given to the first

parameter. Hence the algorithm has to include the feature of fixing the root node or

the start up feature identification and fixation.


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3. Ordering of Parameters

After identification and fixing of startup feature in Web 3.0 products for

students , faculty and IT professionals the consequent features to be considered and

incorporated should be decided on and hence this ordering of parameters has to be

included in the enhanced algorithm.

5.3 ORDERED MAXIMUM SPANNING TREE ALGORITHM

5.3.1 Preliminaries

5.3.1.1 Correlation

Correlation coefficient is a measure of the strength of the linear relationship

between two variables that is defined in terms of the (sample) covariance of the

variables divided by their (sample) standard deviations.

Correlation (r) = [ NΣXY – (ΣX)(ΣY) / Sqrt ( N Σ X2 – (ΣX)

2 [ N Σ Y

2 – (Σy)

2 ] ) ]

N - No of Values

X - First Composite attribute

Y - Second Composite attribute

ΣXY - Sum of product of first and second composite

attribute

ΣX - sum of first composite attribute

ΣY - Sum of second composite attribute

ΣX2

- Sum of Square of first composite attribute

ΣY2

- Sum of square of second composite attribute


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Correlation Coefficient is computed for all composite attributes among

Students, Faculty & IT Professionals to find out the degree of relationship between

the composite attributes for categories.

Let GmCop represent the correlation among the composite attributes in the

dataset.

m = 1 to 3

m=1 represents Students

m=2 represents Faculty

m=3 represents IT Professionals

o = 1 to 24

p= 1 to 24

Correlation Coefficient among Students

Table 5.2 Correlation coefficient in

Media cluster for Students

P11 P12 P13 P14

P11 1 0.046 -.100 0.085

P12 .046 1 .024 .324

P13 -.100 .024 1 -.017

P14 .085 .324 -.017 1

Table 5.3 Correlation coefficient in

Application cluster for Students

P15 P16 P17 P18 P19

P15 1 .095 .018 .004 .026

P16 .095 1 .164

** .142

* .033

P17 .018 .164

** 1 .059 .107

P18 .004 .142

* .059 1 .149

*

P19 .026 .033 .107 .149

* 1


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Table 5.2 and Table 5.3 shows the correlation coefficient among students for

Media cluster and Applications Cluster. High correlation is observed among 3D

(P12 ) and Audio (P14) in Media cluster and Semantic Maps and Semantic Wiki in

Applicatins Cluster. Hence these attributes must be given top priority during design

and development of software products for Students in Media cluster and Applications

cluster.

Correlation Coefficent among Faculty

Table 5.4 Correlation coefficient in Media cluster for Faculty

P11 P12 P13 P14

P11 1 .197

** .076 .106

*

P12 .197

** 1 .068 .162

**

P13 .076 .068 1 -.064

P14 .106

* .162

**

-

.064 1

Table 5.5 Correlation coefficient in Application cluster for Faculty

P15 P16 P17 P18 P19

P15 1 .043 .074 .102 .139

**

P16 .043 1 -.037

.198*

*

.007

P17 .074 -.037 1 -.013 .127

*

P18 .102 .198

** -.013 1 .066

P19 .139*

*

.007 .127* .066 1


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Table 5.4 and Table 5.5 shows the correlation coefficient among Faculty for

Media cluster and Applications Cluster. High correlation is observed among 3D

(P12 ) and Speech recognition (P13) in Media cluster and Semantic Maps and E

decisions in Applications Cluster. Hence these attributes must be given top priority

during design and development of software products for Faculty in Media cluster and

Applications cluster.


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Correlation coefficient among IT professionals

Table 5.6 Correlation coefficient in Media cluster for IT Professionals

P11 P12 P13 P14

P11 1 .179

** .038 .278

**

P12 .179

** 1 .182

** .249

**

P13 .038 .182

** 1 .017

P14 .278

** .249

** .017 1

Table 5.7 Correlation coefficient in Application cluster for IT Professionals

P15 P16 P17 P18 P19

P15 1 .340

** .143

** .126

* .121

*

P16 .340

** 1 .102

* .272

** .283

**

P17 .143

** .102

* 1 .025 .038

P18 .126

* .272

** .025 1 .260

**

P19 .121

* .283

** .038 .260

** 1

Table 5.6 and Table 5.7 shows the correlation coefficient among IT

Professionals for Media cluster and Applications Cluster. High correlation is

observed among 2D (P12 ) and Audio (P14) in Media cluster and Multilingual (P15)

and Semantic Maps(P16) in Applications Cluster. Hence these attributes must be

given top priority during design and development of software products for IT

Professionals in Media cluster and Applications cluster.


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5.3.1.2 Feature Selection

A process of feature selection is applied to remove the edge pairs with

negative correlation and their associated edges to obtain a spanning tree.

Table 5.8 Feature selection in Media Cluster for Students category

Media

Source Destination Weight

p12 p14 0.324

p11 p14 0.085

p11 p12 0.046

p12 p13 0.024

p13 p14 -0.017

p11 p13 -0.1

Table 5.8 shows there is negative correlation among Speech Recognition

(P13) and Audio (P14) & 2D(P11) and Speech Recognition(P13) . Hence the

associated edge 3D(P12) and Speech Recognition (P13) is also removed and

spanning tree is obtained from remaing three pairs of edges 3D (P12) and

Audio(P14), 2D(P11) and Audio (P14) and2D (P11) and 3D( P12).

Table 5.9 Feature selection in Media cluster for Faculty category

Media


p11 p12 0.197

p12 p14 0.162

p11 p14 0.106

p11 p13 0.076

p12 p13 0.068

p13 p14 -0.064


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Table 5.9 shows there is negative correlation among Speech Recognition

(P13 ) and Audio(P14) . Hence the associated edge 2D(P11) and Speech

Recognition(P13),3D (P12) and SpeechRecognition (P13) & Speech

Recognition(P13) and Audio(P14) is also removed and a spanning tree is obtained

from remaing three pairs of edges 2D(P11) and 3D(P12) ,3D (P12) and Audio(P14)

and 2D(P11) and Audio(P14).

Table 5.10 Feature selection in Applications cluster for Faculty category

Applications


p16 p18 0.198

p15 p19 0.139

p17 p19 0.127

p15 p18 0.102

p15 p17 0.074

p18 p19 0.066

p15 p16 0.043

p16 p19 0.007

p17 p18 -0.013

p16 p17 -0.037

Table 5.10 shows that there is a negative correlation among Semantic

wiki(P17) and E Decisions(P18) & Semantic Maps(P16) and Semantic wiki (P17).

Since there are no associated vertices spanning tree is computed among

SemanticMaps (P16) and EDecisions(P18), Multilingual (P15) and Software Agent

(P19), Semantic wiki (P17) and Software Agent (P19), Multilingual (P15) and

EDecisions (P18) , Multilingual (P15) and Semantic wiki (P17) , EDecisions (P18)

and Software Agent (P19), Multilingual (P15) and Semantic Maps (P16),

SemanticMaps (P16) and Software Agent (P19).


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Attributes with a subset of positive Correlation Coefficient alone was considered for

developing the model.

5.4 Experimental Evaluation of Ordered Maximum Spanning Tree

The following are the advantages of Ordered Maximum Spanning Tree with

Backtracking Model(OMSTB)

Root node is automatically determined.

Relative correlation of the nodes are considered

Complexity is

– ( n-2) * ( n*n – ( n*(n+1)/2) )

– (Approx ) n3

5.5 Results and Discussions

Students Category Output Cluster

Original Graph Prims/Kruskals

/Boravka’s OMSTB

Figure 5.1 Result obtained using OMSTB in Output Cluster Student Category

Figure 5.1 depicts Students category output cluster has negative correlation

between Custom Mash up (P23) and Result as Mash up (P24). Hence Custom Mash

P23

P23

P23


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up (P23) is only considered for inclusion of the model for Student category as the

product is targeted towards customized Web 3.0 products for Students.

Students Category Input Cluster


/Boravka’s OMSTB

Cost obtained 0.035 0.035

Figure 5.2 Comparison of Result obtained using OMSTB in Input Cluster

Student Category

Figure 5.2 depicts Students Input which has a correlation coefficient of

0.035. When Ordered Maximum Spanning Tree With Backtracking is applied it is

ordered and included in the model in order Query (P21) and Match Making aspect

(P22). When Prims/ Kruskal’s / Borovka’s was applied Query (P21) and Match

making aspect (P22) to be included is obtained without ordering.

P22

P21

0.035 0.035

P22

P21

0.035

P22

P21


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Students Category Media Cluster

Original Graph

Figure 5.3 Original Graph in Media Cluster Student Category

Figure 5.3 depicts Original Graph obtained in Web 3.0 Data set for Media

Cluster Student Category with the edge cost of 0.046 between P11 and P12 , 0.324

cost between P12 and P14 and 0.085 between P11 and P14.


Prims/Kruskal’s /Boravka’s

Cost : 0.409

Figure 5.4 Prims/Kruskals/Borovka’s Spanning Tree obtained in Media

Cluster Student Category

P11

P12

P14

0.046

0.085

0.324

0.324 0.164

0.085 0.164

P12

P14

P11


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Figure 5.4 depicts Prims / Kruskals / Borovka’s spanning tree obtained in

Media cluster Student category with P12, P14 and P11. An edge cost of 0.324

between P12 and P14 . 0.085 between P14 and P11 was obtained with a cost of 0.409.

Here Ordering and root node is not defined.


OMSTB

Cost :0.409

Figure 5.5 OMSTB Spanning tree obtained in Media Cluster Student Category

Figure 5.5 depicts Students Media spanning tree after applying Ordered

Maximum Spanning Tree With Backtracking which had a correlation coefficient of

0.046 among P11 and P12 and 0.324 among P12 and P13 and 0.085 among P14 and

P11 results in a maximum spanning tree model with a cost of 0.409. While applying

Prims/Kruskals and Borovkas the same cost was obtained and has an ordering of 3D

( P12) followed by Audio (P14) and 2D (P11).

P12

P14

P11

0.324 0.164

0.085 0.164


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Students Category Applications cluster

Original Graph

Figure 5.6 Original Graph in Application Cluster Student Category

Figure 5.6 depicts orginal Graph as per Web 3.0 data set for Application

Cluster Student Category. With 5 vertices and 10 edges from this original graph a

maximum spanning tree has to be generated.

P16

P19 P18

P17 P15

0.095

0.107

0.142 0.033

0.018

0.004

0.149

0.026 0.059

0.164


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Prims/Kruskals /Boravka’s

Cost :0.55

Figure 5.7 Prims/Kruskals/Boravkas Spanning Tree in Application Cluster

Students category

Figure 5.7 depicts Prims/Kruskals/Boravkas spanning tree in Application

Cluster Student Category with a cost of 0.095 between P16 and P15, 0.164 between

P16 and P17 , 0.142 between P16 and P18 & 0.149 between P18 and P19 with a total

cost of 0.55 which is unordered and also without root node fixing.

0.142

0.164 0.095

0.149

P16

P19 P18

P17 P15


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OMSTB

Cost : 0.424

Figure 5.8 OMSTB Spanning Tree in Application Cluster Student Category

Figure 5.8 depicts Ordered Maximum Spanning Tree With Backtracking

Spanning Tree in Application Cluster Student Category with a cost of 0.424 which is

ordered and maximizing the interattribute correlation. From Figure 5.8 it is inferred

that Semantic Maps is the most important for Students in Application cluster

followed by Semantic Wiki, Software Agents, E Decisions and Multiliguality .

P16


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Students Category Platform Cluster


/Boravka’s

OMSTB

Figure 5.9 Result obtained using OMSTB in Platform Cluster Student Category

Figure 5.9 depicts Students Platform which has a correlation coefficient of

.22 with the previous Student category Application cluster Multilingual parameter

and is included in the Ordered Maximum Spanning Tree With Backtracking model .

Students Category Generic cluster


Figure 5.10 Original Graph in Generic Cluster Student Category

P20 P20 P20


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Figure 5.10 depicts Original Graph in Generic Cluster of Student category

with 10 vertices and 38 edges.

Students Category Generic Cluster

Prims/Kruskals /Boravka’s

Cost : 3.458

Figure 5.11 Prims/Kruskals/Boravkas Spanning Tree in Generic Cluster

Student Category

Figure 5.11 depicts Prims/Kruskals/Boravkas Spanning Tree in Generic

Cluster Student Category with a cost of 3.458.

P1

0.25

2

0.308

P2

P4

P3 P10 P7

P5

P8

P6 P9

0.32

8

0.14

1

0.09

7

0.47

0

0.27

6

0.25

8 0.45

5


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OMSTB

Cost : 2.401

Figure 5.12 OMSTB Spanning Tree obtained in Generic Cluster

Student category

Figure 5.12 depicts application of Ordered Maximum Spanning Tree

With Backtracking is visible in the Generic group as it involves 38 edges and 10

vertices. In traditional models had a cost of 3.458 while using ordered maximum

spanning tree with backtracking model the cost obtained is 2.401 with high inter

attribute correlation , ordering , start node and end node identification. Hence from

figure 5.12 privacy (P3) has to be followed by Site Loyality (P4), Omnipresent (P7) ,


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Usability (P2) , Personalizaion (P10), Downloading (P8), Uploading (P9), Fault

Tolerance (P6), Effort (P5) and Time to Produce Results (P1).

Faculty Category Output Cluster

Original Graph Prims/Kruskals /Boravka’s

Correlation Cost obtained 0.196

OMSTB

0.196

Figure 5.13 Comparison of Result obtained using OMSTB in Output Cluster

Faculty Category

Figure5.13 depicts Faculty output cluster, a cost of 0.196 between

Custom mash up (P23) and Result as mash up (P24) is obtained. A Spanning Tree

without ordering is obtained in Prims/Kruskals/Boravkas. While applying Ordered

Maximum Spanning Tree with Backtracking Model an order Custom mash up and

Result as Mash up Spanning Tree was obtained for Faculty model.

0.196 0.164

P 23

P24

0.196 0.164

P 23

P24

P 23

P24

0.196 0.164


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Faculty Category Media Cluster

Original Graph

Figure 5.14 Original Graph in Media Cluster Faculty Category


Cluster Student Category with the edge cost of 0.197 between P11 and P12 , 0.162

cost between P12 and P14 and 0.106 between P11 and P14.


Prims/Kruskals/Boravka’s

Cost:0.359

Figure 5.15 Prims/Kruskals/Boravkas Spanning Tree in Media Cluster

Faculty category

0.197 0.164

0.162 0.164

P11

P12

P14

P11

P12

P14

0.106

0.197

0.162


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Figure 5.15 depicts Prims / Kruskals / Borovka’s spanning tree obtained in

Media cluster Student category with P12, P14 and P11. An edge cost of 0.197

between P11 and P12 . 0.162 between P12 and P14 was obtained with a cost of 0.359.

Here Ordering and root node is not defined.


OMSTB

Cost:0.359

Figure 5.16 OMSTB Spanning Tree in Media Cluster Faculty category

Figure 5.16 depicts Faculty Media spanning tree after applying ordered

maximum spanning tree with backtracking which had a correlation coefficient of

0.197 among P11 and P12 and 0.162 among P12 and P14 results in a maximum

spanning tree model with a cost of 0.359. In Prims/Kruskals and Borovkas the same

cost 0.359 is obtained but has an ordering of 2D ( P11) followed by 3D (P14) and

Audio (P13).

P11

P12

P14

0.197 0.164

0.162 0.164


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Faculty Category Input Cluster


Correlation Cost obtained 0.185

OMSTB

0.185

Figure 5.17 Comparison of Result obtained using OMSTB in Input Cluster

Faculty category

Figure 5.17 depicts Students Input which has a correlation coefficient

of 0.185. When Ordered Maximum Spanning Tree With Backtracking is applied it is

ordered and included in the model in order Query (P21) and Match Making aspect

(P22). When Prims/ Kruskal’s / Borovka’s was applied Query (P21) and Match

making aspect (P22) to be included is obtained without ordering.

P21 P22

0.185

P21 P22

0.185

P21 P22

0.185


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Faculty Category Applications Cluster

Original Graph

Figure 5.18 Original Graph in Application Cluster Faculty Category

Figure 5.18 depicts original Graph as per Web 3.0 data set for Application

Cluster Faculty Category. With 5 vertices and 8 edges from this original graph a

maximum spanning tree has to be generated.


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Cost :0.566


Faculty Category

Figure 5.19 depicts Prims/Kruskals/Boravkas spanning tree in Application

Cluster Faculty Category with a cost of 0.198 between P16 and P18, 0.102 between

P18 and P15, 0.139 between P15 and P19 & 0.127 between P19 and P17 with a total

cost of 0.566 which is unordered and without root node fixing and without

considering inter attribute correlation.

0.198

P16

P15

P19 P18

P17

0.127 0.102

0.139


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OMSTB

Cost 0.566

Figure 5.20 OMSTB Spanning Tree in Application Cluster Faculty Category

Figure 5.20 depicts Ordered Maximum Spanning Tree With Backtracking

Spanning tree in Application Cluster Faculty Category with a cost of 0.566 which is

ordered and maximizing the interattribute correlation. From Figure 5.20 it is inferred

that Semantic Maps (root node) is the most important Web 3.0 for Faculty in

Application cluster followed by EDecisions, Multilngual, Software Agent and

Semantic Wiki .


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Faculty Category Platform Cluster


/Boravka’s

OMSTB

Figure 5.21 Result obtained using OMSTB in Platform Cluster Faculty

Category

Figure 5.21 depicts Faculty Platform which has a correlation

coefficient of .069 with the Category Faculty Cluster Applications Parameter

Semantic wiki is included in the Ordered Maximum Spanning Tree With

Backtracking model

P20 P20

P20


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Faculty Category Generic Cluster

Original graph

Figure 5.22 Original Graph in Generic Cluster Faculty Category

Figure 5.22 depicts the original Graph in Generic cluster for Faculty

category with 10 vertices and 43 edges.


91

Faculty category Generic cluster


Cost : 1.986

Figure 5.23 Prims/Kruskals/Boravkas Spanning Tree in Generic Cluster

Faculty Category

Figure 5.23 depicts Prims/Kruskals/Boravkas graph in Generic Cluster

Student Category with a cost of 1.986.

.164

P2

P5

P9 P3 P10

P7

P6

P1

P4

P8

.284 .219 .253

.283 .195

.193

.193

.202


92

Faculty category Genric cluster

OMSTB

Cost 1.806

Figure 5.24 OMSTB Spanning Tree obtained in Generic Cluster Faculty

Category

Figure 5.24 depicts application of Ordered Maximum Spanning Tree With

Backtracking is visible in the Generic group as it involves 43 edges and 10 vertices.

In traditional models the model obtained is shown above which had a cost of 1.986

and as per Figure 5.24 while using Ordered Maximum Spanning Tree With

Backtracking model the cost obtained is 1.806 with high inter attribute correlation ,

ordering , start node and end node identification. Hence Usability (P2) followed by

Personalization (P10), Uploading (P9), Time to produce results (P1), Downloading

(P8), Fault tolerance (P6), Effort (P5), Omni present (P7), Privacy (P3), Site loyality

(P4) is included in the model for Faculty.

.146


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IT Professionals category Output cluster


Cost 0.095

OMSTB

0.095

Figure 5.25 Comparison of Result obtained using Prim’s/ Kruskals/Boravkas

and OMSTB in Output Cluster IT Professionals Category

Figure 5.25 depicts IT Professionals output graph which has

correlation of 0.095 between P23 and P24. Hence custom Mash up and Result as

Mash up are included in order.

P 23

P24

0.095 0.164

P 23

P24

0.095 0.164

0.095 0.164

P 23

P24


94

IT Professionals Category Media cluster

Original Graph

Figure 5.26 Original Graph in Media Cluster IT Professionals Category


Cluster IT Professionals Category with 4 vertices and 6 edges from which a Spanning

tree has to be obtained.

P11

P12

P14

P13

0.278

0.038

0.017

0.249

0.179

0.182


95

IT Professionals category Media Cluster


Cost:0.709

Figure 5.27 Prims/Kruskals/Boravka’s Spanning Tree obtained in Media

Cluster IT Professionals Category

Figure 5.27 depicts Prims / Kruskals / Borovka’s spanning tree

obtained in Media cluster IT Professionals category with P11, P14 and P12, P13. An

edge cost of 0.278 between P11 and P14 , 0.249 between P14 and P12 and 0.182

between P12 and P13 was obtained with a cost of 0.709. Here Ordering and root node

is not defined.

0.278 0.164

0.249 0.164

0.182 0.164

P11

P14

P12

P13


96

IT Professional Category Media Cluster

OMSTB

Cost : 0.709

Figure 5.28 OMSTB Spanning Tree obtained in Media Cluster IT

Professionals Category

Figure 5.28 depicts IT Professionals Media spanning tree which has a

correlation of 0.278 among P11 and P14 , 0.249 among P14 and P12 , 0.182 among

P12 and P13 results in a maximum spanning tree model with a cost of 0.709 in

Prims/Kruskals and Borovkas and 0.709 among P14 and P11 which has the same cost

but has an ordering of 2D (P11) followed by Audio (P14) ,3D (P12) and Speech

Recognition (P13).

P11

P14

P12

P13

0.278 0.164

0.249 0.164

0.182 0.164


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IT Professionals Category Input Cluster


/Boravka’s

OMSTB

Correlation Cost obtained 0.381 0.381

Figure 5.29 Comparison of Result obtained using OMSTB in Output Cluster

in IT Professionals Category

Figure 5.29 depicts IT Professionals Input graph which has a

correlation coefficient of 0.381 hence it is included in the model in order Query

(P21) and Match Making aspect (P22). When Prims/ Kruskal’s / Borovka’s was

applied Query and Match making aspect (P22) to be included is obtained without

ordering.

0.381 0.164

P21

P22

0.381 0.164

P21

P22

0.381 0.164

P21

P22


98

IT Professionals category Applications Cluster

Original Graph

Figure 5.30Original Graph in Applications Cluster IT Professionals Category

Figure 5.30 depicts Original Graph as per Web 3.0 data set for Application

Cluster IT Professionals Category with 5 vertices and 10 edges. From this original

graph a maximum spanning tree has to be generated.


99

IT Professionals Category Applications Cluster

Prims/Kruskals/Borovka’s

Cost:1.038


IT Professionals Category

Figure 5.31 depicts Prims/Kruskals/Boravkas Spanning Tree in

Application Cluster IT Professional Category with a cost of 0.143 between P15 and

P17, 0.340 between P15 and P16, 0.283 between P16 and P19 and 0.272 between P16

and P18 and with a cost of 1.038 which is unordered and also not considering the

inter attribute correlation.

0.143 0.340

P15

P19

P16 P17

P18

0.283 0.272


100

IT Professionals Category Applications Cluster

OMSTB

Cost :0.908

Figure 5.32 OMSTB Spanning Tree in Application Cluster IT Professionals

Category

Figure 5.32 depicts Ordered Maximum Spanning Tree With

Backtracking graph in Application Cluster IT Professionals Category with a cost of

0.908 which is ordered and maximizing the interattribute correlation. From Figure

5.32 it is inferred that Multilinguality (P15) the root node is the most important for r

IT Professionals in Application cluster followed by Semantic Maps (P16) , Software

Agent (P19), EDecisions V(P18) , Semantic Wiki (P17).


101

IT Professionals Category Platform Cluster


/Boravka’s

OMSTB

Figure 5.33 Result obtained using OMSTB in Platform Cluster IT

Professionals category

Figure 5.33 depicts IT Professionals Platform which has a correlation

coefficient of 0.089 with the previous IT Professionals category Application Cluster.

Semantiwiki is included in the Ordered Maximum Spanning Tree With Backtracking

model.

P20 P20 P20


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IT Professionals Category Generic Cluster


Figure 5.34 Original Graph in Generic Cluster IT Professionals Category

Figure 5.34 depicts Original Graph in Generic Cluster IT Professionals

Category with 10 vertices and 42 edges.


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Cost : 3.613

Figure 5.35 Prims/Kruskals/Boravkas Spanning Tree in Generic Cluster IT

Professionals Category

Figure 5.35 depicts Prims/Kruskals/Boravkas Spanning Tree in

Generic Cluster IT Professionals Category with a cost of 3.613.

P2

P8

.270

P9 P7

P2

P6 P3

P1 P4 P10

.712 .336

.317

.415 .487

.238 .518 .320


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OMSTB

Cost :3.25

Figure 5.36 OMSTB Spanning Tree obtained in Generic Cluster

IT Professionals category

Figure 5.36 depicts application of Ordered Maximum Spanning Tree

With Backtracking is visible in the Generic group as it involves 42 edges and 10

vertices. The model obtained in traditional algorithms which had a cost of 3.613

while using Ordered Maximum Spanning Tree With Backtracking model the cost

obtained is 3.25 with high inter attribute correlation , ordering start node and end

node identification. Hence Downloading (P8) followed by Uploading (P9),


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Omnipresent (P7), Usability (P2), Privacy (P3), Site loyality (P4), Fault tolerance

(P6), Effort (P5), Time to produce results (P1) and Personalization (P10).

5.5 SUMMARY

Ordered Maximum Spanning Tree With Backtracking focuses on the inter

attribute correlation rather than over all cost maximization which aids in the design

and development of a logical model in Web 3.0 for Students, Faculty and IT

Professionals. Ordered Maximum Spanning Tree With Backtracking algorithm when

applied in Web 3.0 Data set for Students, Faculty and IT Professionals results in a

logical model which facilitates the design and development of Web 3.0 products for

Students, Faculty and IT Professionals.

The major contribution of the proposed work is to preserve and maximize

the inter attribute correlation of the vertices in a Maximum spanning tree model

which may lead to a cost equivalent to traditional algorithms or may have less cost

when compared to Prims/Kruskals/Borovkas.


Documents

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