Towards Discriminant Analysis Modeling of Web 3.0 Design and Development for Students, Faculty and IT Professionals

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 9, September 2011

Towards Discriminant Analysis Modeling
of Web 3.0 Design and Development for
Students, Faculty and IT Professionals
S.Padma and Dr.Ananthi Seshasaayee

S..Padma,
Research scholar,
Bharathiar University Dr.Ananthi Seshasaayee
Coimbatore. Associate Prof. &Head,
Assistant Professor Quaid-e-Millath Govt.
Vels University College for women,
Chennai . Chennai
INDIA. INDIA
padmanivasan@gmail.com ananthiseshu@gmail.com

applications to different domains. Web 3.0
Abstract provides integrated real time application
environment to the user. The applications are
Web 3.0 is an evolving extension of the web 2.0 majorly involved in searching using semantic
scenario. The perceptions regarding web 3.0 is web, 3D web and are media centric. Web 3.0
different from person to person . Web 3.0 supports pervasive components. Each component
Architecture supports ubiquitous connectivity, and its relations are represented below.
network computing, open identity, intelligent web,
distributed databases and intelligent applications .
In web 3.0, web is transformed into database or
Some of the technologies which lead to the design
and development of web 3.0 applications are Data Web wherein the data which are published
Artificial intelligence, Automated reasoning, in the web is reusable and can be queried. This
Cognitive architecture, Semantic web . An attempt is enables a new level of data integration and
made to capture the requirements of Students, application interoperability between platforms. It
Faculties and IT professionals regarding Web 3.0 also makes the data openly accessible from
applications so as to bridge the gap between the anywhere and linkable as web pages do with
design and development of web 3.0 applications and hyperlinks. Data web phase is to make available
requirements among Students, Faculties and IT structured data using RDF[1]. The scope of both
professionals. Discriminant modeling of the
structured and unstructured content would be
requirements facilitate the identification of key areas
in the design and development of software products covered in the full semantic web stage. Attempts
for Students, Faculties and IT professionals in Web will be to make it widely available in RDF and
3.0. OWL semantic formats.

Keywords : Web 3.0, Discriminant analysis , Design The driving force for web 3.0 will be artificial
and Development ,Model intelligence. Web 3.0 will be intelligent systems
or will depend on emergence of intelligence in a
I INTRODUCTION more organic fashion and how people will cope
with it. It will make applications perform logical
Web 3.0 is an extension of www, in which the reasoning operations through using sets of rules
information can be shared and interpreted by expressing logical relationships between
other software agent to find and integrate concepts and data on the web. With the

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 9, September 2011

realization of the semantic web and its concepts services architecture. It surveys base fault
web 3.0 will move into Service Oriented tolerance mechanisms and shows how they are
Architecture. adapted to deal with the specifics of the web in
the light of ongoing work in the area[5]. Barry
The evolution of 3D technology is also being Norton, Sam Chapman and Fabio Ciravegna
connected to web 3.0 as web 3.0 may be used on discussed in the paper developing a Service-
massive scale due to its characteristics. Oriented Architecture to Harvest information for
the Semantic web which discusses about the
Armadillo architecture, how it is reinterpreted as
Web 3.0 is media centric where users can locate
workow templates that compose semantic web
the searched media in similar graphics and sound
services and show how the porting of Armadillo
of other media formats.
to new domains, and the application of new
tools, has been simplified[6].
The pervasive nature of web 3.0 makes the users
of web in wide range of area be reached not only
in computers and cell phones but also through III PROBLEM DEFINITION
clothing, appliances, and automobiles.
The Design and Development of web 3.0
products are on the course. Due to the existence
II REVIEW OF LITERATURE
of the ambiguity in the requirements of Students,
Faculty and IT professionals for structuring the
Claudio Baccigalupo and Enric Plaza discussed web 3.0 products , bridging the gap between web
in the paper poolcasting : a social web radio 3.0 developers and Students, Faculty and IT
architecture for Group Customization about Pool professionals is required. The key factors for
casting a social web radio architecture in which each of these three categories students ,faculty
groups of listeners influence in real time the and it professionals are to be identified and their
music played on each channel. Pool casting users preference order is to be extracted.
contribute to the radio with songs they own,
create radio channels and evaluate the proposed
music, while an automatic intelligent technique Let G1, G2, G3 denote the three groups in web
schedules each channel with a group customized 3.0 . The problem is to find the order of
preferences of the three groups for the three
sequence of musically associated songs[2] .
categories Students, Faculty and IT professionals
M.T.Carrasco Benitez discussed in the paper
based on the attributes v1 , v2 , ….. vn included
Open architecture for multilingual social
in these three groups G1, G2 and G3 to facilitate
networking about an open architecture for all the
multilingual aspects of social networking. This the design and development of applications in
architecture should be comprehensive and web 3.0 for the categories.
address well-trodden fields such as localization,
and more advanced multilingual techniquesto IV MATERIALS AND METHODS
facilitate the communication among users[3] .
Autona Gerber, Alta van der Merwe, and We collected the perceptions of students
Andries Barnard discussed in the paper A ,Faculties and IT professionals inline with web
functional Semantic web architecture about the 3.0 attributes. A five point scale was adapted
CFL architecture which depicts a simplification which ranges from very low satisfaction , low
of the original architecture versions proposed by satisfaction, Medium satisfaction, high
Bernes-Lee as a result of the abstraction of satisfaction to very high satisfaction.
required functionality of language layers. Gerber
argues that an abstracted layered architecture for a. Block diagram of Web 3.0 discriminant
the semantic web with well defined modeling
functionalities will assist with the resolution of
several of the current semantic web research
debates such as the layering of language
technologies [4]. Ferda Tartanoglu val’erie
Issarny, Alexander Romanovsky and Nicole
Levy discussed in the paper Dependability in the
web services architecture which lists about how
to build dependable systems based on the web

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b. Steps in Web 3.0 Discriminant modeling

a. Start
b. Collect the perceptions regarding the
attributes of web 3.0 among the three
Start categories Students, Faculty and IT
professionals
c. Classification of attributes into three
groups G1, G2 and G3.
d. Compute Mean and Standard Deviation
for G1, G2 and G3
Collection of e. Correlation Coefficient among the
Perceptions of web groups G1, G2 and G3
f. Discriminant Modeling for the three
3.0 among students, categories Students, Faculty and IT
Faculty and IT professional
professionals g. stop

Classification of c. Preprocessing
attributes into G1,
G2 and G3 The data collected are verified for completeness.
The missing values are replaced with the mean
value.

d. Classification
Mean and Standard
The data collected from the three categories
Deviation for G1, Students , Faculty and IT professionals based on
G2 and G3 the attributes 2D, 3D, Audio, Custom mash up, E
decisions, Multilingual, Result as Mash up,
Semantic Maps, Semantic Wiki, Software
Agents, Speech recognition. Based on the
Correlation among functionality of the attributes they are grouped
into G1 , G2 and G3. G1 comprises of
G1, G2 and G3 Multilingual, Semantic maps, Edecisions,
Semantic wiki and Software agents . G1 is
termed as Applications . G2 comprises of 3D,
Discriminant Audio, 2D and Speech recognition. G2 is termed
modeling for three as Media. G3 comprises of Custom Mash up,
Result as Mash up . G3 is termed as Output.
categories Students,
Faculty and IT
professionals

Stop

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Vol. 9, No. 9, September 2011

The correlation coefficient for all pairs among
the Groups are calculated using the following
formula.[7]

e. Mean and Standard Deviation Correlation(r) =[ NΣXY - (ΣX)(ΣY) /
Sqrt([NΣX2 - (ΣX)2][NΣY2 - (ΣY)2])]
where
N = Number of values or elements
TABLE 1. COMPARISION OF MEAN FOR THE THREE X = perception weightage for 1st group
CATEGORIES Y = perception weightage for 2nd group
ΣXY = Sum of the product of first and
Second group perceptions
ΣX = Sum of 1st group
MEAN
ΣY = Sum of 2nd group
CATEGORY G1 G2 G3 ΣX2 = Sum of square 1st group
STUDENTS 3.94049 3.5794 2.92 ΣY2 = Sum of square 2nd group
FACULTY 3.17 2.95 2.49
TABLE III . CORRELATION AMONG GROUPS FOR
ITPROFESSIONALS 3.97 3.9 3.36 STUDENTS

STUDENTS
G1 G2 G3
For all the three categories G1 Applications has
higher mean when compared to all others. G1 1 0.56 0.74
G2 0.56 1 0.51
TABLE II . COMPARISION OF STANDARD DEVIATION G3 0.74 0.51 1
FOR THE THREE CATEGORIES

TABLE IV .CORRELATION AMONG GROUPS FOR
FACULTY
STANDARD
DEVIATION
CATEGORY G1 G2 G3
FACULTY
STUDENTS 0.57 0.52 0.53
G1 G2 G3
FACULTY 0.57 0.49 0.45
G1 1 0.34 0.34
ITPROFESSIONALS 0.58 0.55 0.39
G2 0.34 1 0.28
G3 0.34 0.28 1

The standard deviation for G3 are comparatively
lower for faculty and IT professionals . Faculty
and IT professionals have similar opinions about
G3 – output. There is no significant difference in
the standard deviation of students. TABLE V. CORRELATION AMONG GROUPS FOR IT
PROFESSIONALS
ITPROFESSIONALS
f. Finding the Correlation Coefficient
G1 G2 G3
Correlation Coefficient reveals the nature G1 1 0.3 0.42
relationship between the attributes. G2 0.3 1 0.32
G3 0.42 0.32 1

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It is evident that all the three groups G1, G2 and
G3 are positively correlated to each other in all
the three categories Students, Faculty and IT
professionals.

g. Discriminant modeling on groups:
Based on the Canonical Discriminant Function
Discriminant Function analysis is a two step coefficient, the linear discrminant equation can
process. be written as

Step 1: A set of discriminant functions are tested TABLE VII CLASSIFICATION RESULTS
for its significance.
Y = -2.339+ 3.247 x1 + 0.885 x2– 0.547 x3 (1)
1.a. A Matrix of total variances and Co variances
are constructed Y = -7.452 -1.017x1 + 0.408x2 + 1.887x3 (2)

1.b. A matrix of pooled within group variances Based on (1) the following are the classification
and Co variances are constructed. results.

1.c. F test is performed on the two matrices
constructed.

1.d Variable which have significantly different
means across the groups are identified.

Step 2. Classification.

In the classification step classification of
variables are done. DA automatically determines
some optimal combination of variables so that
the first function provides the most overall
discrimination between groups and the second
provides the second most and so on. The
functions are independent or orthogonal [8].

V RESULTS AND DISCUSSION

TABLE VI . CANONICAL FUNCTION COEFFICIENTS

Function
1 2
Applications 3.247 -0.01
(x1)
Media (x2) 0.885 0.408
Output (x3) -0.547 1.887
(Constant) -2.339 -7.452

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Classification Results

Group Predicted Group Membership
Students Faculty IT Professionals Total
Students 218 7 8 233

Count Faculty 4 347 6 357
IT Professionals 3 12 421 436

Original
Students 93.5 7.3 4.7 100

% Faculty 1.1 97.2 1.7 100
IT Professionals 0.7 2.8 96.6 100

a. 95.8% of original grouped cases correctly The order of preferences for the three categories is
classified. given below based on the above Classification
Function Coefficients.

Percentage of correct classification

98 Order of Preferences among groups for Students
97
96 Percentage of
95 correct
94
93 classification
92
91
al
lty
ts

io n
en

cu
ud

ss
St

ofe
pr
IT

TABLE VIII CLASSIFICATION FUNCTION COEFFICIENTS

Categories
Students Faculty ITProfessionals

Applications 14.048 3.743 4.818

Media 9.374 6.097 7.488
:
Output 8.074 7.66 12.521

(Constant) -46.475 -29.192 -49.519

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Vol. 9, No. 9, September 2011

From the above three tables the design and
development of web 3.0 products specifically
related to Students, Faculty and IT professionals can
ensue the group preference orders and attributes .
The products can be designed with the maximum
attributes in the first group preference followed by
lesser attributes in the second and third group.

VI CONCLUSION

The perceptions inline with web 3.0 are collected
from students, Faculty and IT professionals. The
data’s are preprocessed , classified, Mean, Standard
Order of preferences among groups for Faculty deviation and correlation coefficient are computed
to understand the descriptive and Discriminant
modeled. At the outset of evolving growth in Web
3.0 this model is an initiative for the of web 3.0
product design for Students , Faculty and IT
professionals.

REFERENCES

[1].
en.wikipedia.org/wiki/Resource_Description_Frame
work

[2] Baccigalupo C.; Plaza E.; Poolcasting: a Social
Web Radio Architecture for Group
Customisation.Computer.org
2007.http://portal.acm.org/citation.cfm?id=1332471.
1332755.

Order of preferences among groups for IT [3]. ]. Carrasco Benitez M.T;, Open architecture for
Professionals multilingual social networking. Position paper for
the W3C workshop on the Future of Social
Networking [FSN], 15-16 January 2009.

[4]. . Aurona Gerber; Alta van der Merwe ; Andries
Barnard; A functional semantic architecture.

http://www.eswc2008.org/final-pdfs -for-web-
site/fisr-1.pdf.

[5]. Ferda Tartanoglu val’erie Issarny; Alexander
Romanovsky and Nicole Levy er; Dependability in
the web services architecture. 2007
.http://citeseerx.ist.psu.edu/viewdoc/download?
doi=10.1.1.14.771&rep=rep1&type=pdf.

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[6]. Barry Norton; Sam Chapman ; Fabio Ciravegna;
Developing a Service- Oriented Architecture to
Harvest information for the Semantic web .2005.

[7]. http://easycalculation.com/statistics/learn- S.Padma , is a research scholar in
correlation.php Bharathiar university , Coimbatore.
She has published 2 international
[8]. journals . Her area of interests are
http://www.statpower.net/Content/312/Lecture Web mining.
%20Slides/Discriminant%20Analysis.pdf

[9]. Production of Cross Media Content for Multi-
Channel Distribution, 2007, AXMEDIS apos;07 Dr. Ananthi Seshasaayee received her
Third International Conference Volume ,Issue , 28- Ph.D in Computer Science from Madras
30 Nov . 2007 Page(s):115-122. University. At present she is working
as Associate professor and Head,
Department of computer science, Quaid-
[10 ] Mantovani.F; VR Learning : Potential and e-Millath Government College for
Challenges for the Use of 3D Environments in Women, chennai. She has published 17
Education and Traning . 2001. international journals. Her area of interest involve the
fields of Computer Applications and Educational
[11]. Andr.P.Freire; Renata; P.M.Fortes ;An technology.
Evaluation of Web Accessibility Metrics based on
their Attributes .2008.

[12]. Nuno Laranjeiro; Towards Fault Tolerance in
Web Services Compositions .2007.

[13]. Dhiraj Joshi ; Ritendra Datta ; Ziming Zhuang
;PARAgrab : A Comprehensive Architecture for
Web Image .2006.

[14]. Antonio Tapiador; Antonio Fumero, Joaqu’m
Salvach’ua; Sandra Aguirre; A Web Collaboration
Architecture .2006.

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