Effective Semantic Web Service Composition Framework Based on QoS by R.Sethuraman Research Scholar Sathyabama University

1. by
R.Sethuraman
Research Scholar
Sathyabama University
Guided By
Dr.T. Sasipraba.,Ph.D.,
Professor & Dean,
(Research & Development),
Effective Semantic Web Service Composition
Framework Based on QoS

2. Agenda
• Introduction
-Web services
-Semantic Web services
• Problem Statement
• Research Objectives
• RoadMap - Research Objectives
• Course work Status
• Workshop Attended
• Publication Details
• Literature Survey
• Inferences from literature survey

3. Introduction
Web services :
• Web service is a reusable and a discoverable software
component interacting between disparate systems using
standard protocols.
Semantic Web services :
• SWS , as originally envisioned, is a system that enables machines to
“Understand” and respond to complex human request based on
their meaning .
• Semantic Web services aim at making Web services as
machine understandable utility.

4. • SWS is that they enable machines to automatically perform
complex tasks by manipulating a series of heterogeneous
Web services based on semantics.
• Most aspects of SWS, such as
1. Automatic discovery
2. Automatic selection,
3. Automatic Composition, invocation, or monitoring
of services are tightly related to the quality of these services (Qos)
• Web services define a semantic description of services
including their functional and non-functional properties.
Semantic Web Services Features

5. Functional and non-functional properties
• Functional properties:
1. Input
2. Output
3. Precondition
4. Effects
• Functional properties (QoS):

6. Problem Statement
• With the rapid improvement of e-Goverence e-Commerce over the
Internet, nowadays web services get more attention among
enterprises and the public.
• Due to the vital growth of web services, more and better web
services are available to satisfy a user’s request on demand.
• Furthermore, competition among web services readily available
to fulfill a request made it more difficult to find and compose the
most appropriate service for a specific application domain.
• Since many service providers are available for similar functionality,
it is a challenge to find the best suitable service for a client’s
requirement during the process of discover and compose.
• This work focuses on designing a QoS driven framework for web
service discovery and composition.

7. Research Objectives
• To propose an framework for QoS Driven services
discovery and selection process.
• To propose an enhanced semantics web service
recommendation algorithm for automated QoS Driven
semantic web services ranking process.
• To implement a mathematical solution for obtaining
the optimal value to semantics Web service
composition.
• Effectively implement the framework on Semantic
Representation in Cloud Services

8. RoadMap - Research Objectives
1.Implement Fuzzy logic.
2.Automate Fuzzy Rules &
Apply Defuzzification using
Machine learning
technique (Association
Rule Learning)
To implement Resource
optimization Techniques
Integer and mixed Integer
Programing
To create Cloud ontology to
represent the semantic in
cloud services

9. Fuzzy Service Discovery techniques
• A fuzzy set à is represented as
Ã= {(x, μÃ(x))| x ∈ X)
• The membership function of the complement of
a fuzzy set (not Ã) denoted as is defined as

10. Fuzzy Service Discovery techniques
• Intersection between two fuzzy sets represents
their common elements.
• Union of two fuzzy sets is the sum of two
memberships excluding duplicated elements.
Ex: QoS parameters membership functions (Reduce
search Space -> “Cheap”Hotel Booking Scenario )

11. Fuzzy Service Discovery
Framework
.
Fuzzy Engine
Fuzzy Rule
Generator
OWL-S KB
ENGINE
Web service 1
Web service 2
Web service 3
USERS
REQ
USERS
REQ
USERS
REQ

12. Semantics Web service Composition
Syntactic Approach:
T = {A,B}->T={A,B,C,D,E} -> T={A,B,C,D,E,F,G,H}
Web
Service
1
A
B
C
D
E
Web
Service
2
Web
Service
3
Web
Service
3
F
G
H
F
K
L
A
C
E
C
HH

13. Semantics Web service Composition
Semantic Approach:
Given
Knowledge
T
D E
Web
Service
1
Web
Service
2
Web
Service
2
F
G
H
F
K
L
A
C
E
C
HH

14. Mathematical solution for Service Composition
To implement semantic Web service composition
problems using Integer Linear Programming (ILP).
Domain definition:
•W is the set of Web services in a UDDI system.
• P is the set of all Web service parameters in W.
•P(In) P⊆ , is the set of parameters that are used as
input for any Web service.
•P(out) P⊆ , is the set of parameters that are used as
output for any Web service.
•P (Initial) P , is the set of initially given parameters.⊆

15. Mathematical solution for Service Composition
• P (Goal) P , is the set of goal parameters.⊆
• Stage (s): 1 ≤ s ≤ S , where S is the maximum number
of stages for Web service composition. Ws is the set
of Web services simultaneously invoked at Stage s.
• If S = 1 and |W1| = 1, then the problem is a Web
Services Discovery Problem; otherwise,
• if (S > 1 or |W| > 1 ), then it is a Web Services
Composition Problem.

16. Decision Variables
• Invocation variables
• usage variables
1. input parameters
2. output parameters
3. known-unused parameters
Parameters can be used as input parameters or output
parameters in a stage, or they are carried to the next
stage, in which case parameters would be known-
unused parameters.

17. Objective Function
Any numerically describable QoS factors can form the
objective function.
where ‘f’ is the function of Web service ‘w’ and stage
‘s’.

18. Problem Constraints
• Initial knowledge constraints
• Goal knowledge constraints
• Web services invocation
constraints
• Non-concurrency constraints

19. Semantic Representation in Cloud Services

20. Cloud Service Ontology

21. Cloud Provider Ontology - Open Stack

22. OWL-S Annotation- Open Stack

23. Resources Configurations- Open Stack

24. Course work program (2013-2015)
Year 2013-14
Research Methodology -Completed
Advance Optimization Techniques -
Completed
• Year 2014-15
Web services - Completed
Knowledge Engineering - Completed

25. Workshop Attended
Attended 10 Days Training Programme on Data
Analytics to be held at Sathyabama University
Sponsored by Big Data Initiative | Department Of
Science & Technology (DST-BDI) Sponsored
Duration Period: 27th April 2016 to 7th May 2016.

26. Paper Publication
• “Multi-Channel E-Learning System based on
Semantic Web Service Architecture” pp. 7257-7264
International Journal of Applied Engineering
Research (IJAER) Volume 9, Number 20 (2014)
Authors: R.Sethuraman and Dr.T.Sasiprabha
• “An Effective QoS Based Web Service Composition
Algorithm for Integration of Travel & Tourism”
Resources Volume 48, 2015, Pages 541-547 Procedia
Computer Science International Conference on
Computer, Communication and Convergence (ICCC
2015) Authors: R.Sethuraman and Dr.T.Sasiprabha

27. Literature Survey
S.No PAPER TITLE,YEAR OF
PUBLISHING AND PUBLISHER
OBSERVATION LIMITATION
1 Dealing with Fuzzy QoS
Properties in Service
Composition 10th Jubilee
IEEE International
Symposium on Applied
Computational Intelligence
and Informatics • May 21-23,
2015
• semantic Web service discovery and
selection algorithm which ranks the
semantically similar or related Web
services based on the service
functionality, capability, QoS and
business offers.
• The semantic broker based Web
service architecture to facilitate the
semantic Web service publishing,
discovery and selection.
• Failed to implement semantic
Web service better ontology
Concept to improve the user
usage experience.
• Results can be improved by
implementing Associative
Rule Mining for popular App
recommendations under
Unsupervised Machine
learning technique
2 Cloud Service Matchmaking
Approaches: A Systematic
Literature Survey 2015 26th
International Workshop on
Database and Expert Systems
Applications
• novelty and significance of this
paper is that distributed and
cooperative agents were used to
create an ontology based self-
organizing service discovery
approach. Load balancing a
significant influencing factor has
been dealt using Delegation concept
which allow for flexibility since they
can expand quickly as the demands
increases.
• The approach did not
implement the concept of
machine learning concept for
the improved user results .
• Rather to acts as semantic
web, implementing as
semantic web services help
in connecting the
Customers(Business service
providers ) and end users

28. Literature Survey
S.No PAPER TITLE,YEAR OF
PUBLISHING AND PUBLISHER
OBSERVATION LIMITATION
3 A Web Service Composition
Framework Using Integer
Programming with Non-
Functional Objectives and
Constraints 2013 IEEE
• Applied Knowledge-graph-based
Clustering algorithm to group
the obtained search results from
Mobile App Repository.
• This approach groups topic
labels from search results into
topic clusters, and then assigns
the apps to these topic label
clusters.
• Failed to implement semantic
Web service better ontology
Concept to improve the user
usage experience.
• Results can be improved by
implementing Associative Rule
Mining for popular App
recommendations under
Unsupervised Machine learning
technique
4 Cloud Services Composition
Through Semantically
Described Patterns Springer
International Publishing
Switzerland 2016
• They have created Varity of
business ontologies and used
semantic web concept in Local
business website logic instead
normal database driven solutions.
• The system retrieves the
unambiguous results based on
user needs instead of Keyword
based driven results
• The approach did not implement
the concept of machine learning
concept for the improved user
results .
• Rather to acts as semantic web,
implementing as semantic web
services help in connecting the
Customers(Business service
providers ) and end users

29. Literature Survey
S.No PAPER TITLE,YEAR OF
PUBLISHING AND PUBLISHER
OBSERVATION LIMITATION
5 Semantic and Matchmaking
Technologies for
Discovering,Mapping and
Aligning Cloud Providers’s
Services December 2013 DOI:
10.1145/2539150.2539204
• The have created framework for
Semantic Web services discovery
based on the Non functional
criteria (QoS)
• Applied personalized algorithm
for ranking the final search
results before giving to the end
users.
• The approach did not implement
the concept any of machine
learning concept for the
improved user results .
.
• Results can be improved by
applying prison and recall
methods.
6 WEB SERVICE SELECTION BASED
ON RANKING OF QOS USING
ASSOCIATIVE CLASSIFICATION
International Journal on Web
Service Computing (IJWSC),
Vol.3, No.1, March 2012
• They have created cloud based
services ontologies based on
newly created clusters in cloud
environment.
• Finding the similarity amount the
clusters is implemented for
better results to user
• The approach did not implement
the concept any of machine
learning concept for the
improved user results .
• Rather to acts as semantic web,
implementing as semantic web
services help in connecting the
Customers(Business service
providers ) and end users

30. Inferences from literature survey
• Need to create Comprehensive and Descriptive ontology for
the problem statement.
• The knowledge based ranking algorithm enhances the results
of prompt solutions.
• Machine Learning Algorithm with supervised and unsupervised
methods are implemented for fine tuning the outcomes
• Non functional parameters are used dynamically discover
,Selection and composing semantic web services for the
better results.
• Different semantic web applications needs to convert the
unstructured or semi structured inputs into XML/RDF formats
to enable easy machine processing.
• To obtaining the optimal value to semantics Web service
composition.

31. References
1. Dealing with Fuzzy QoS Properties in Service Composition 10th Jubilee
IEEE International Symposium on Applied Computational Intelligence
and Informatics • May 21-23, 2015
2. Cloud Service Matchmaking Approaches: A Systematic Literature Survey
2015 26th International Workshop on Database and Expert Systems
Applications
3. A Web Service Composition Framework Using Integer Programming
with Non-Functional Objectives and Constraints 2013 IEEE
4. Cloud Services Composition Through Semantically Described Patterns
Springer International Publishing Switzerland 2016
5. WEB SERVICE SELECTION BASED ON RANKING OF QOS USING
ASSOCIATIVE CLASSIFICATION International Journal on Web Service
Computing (IJWSC), Vol.3, No.1, March 2012
6. SUBDUE -Graph Based Knowledge Discovery- Graphbased Unsupervised
Learning (Discovery) http://ailab.wsu.edu/subdue/ 2016

32. References
8. Semantic cluster based Search in UDDI for Health Care Domain Indian
Journal of Science and Technology, Vol 9(12), DOI:
10.17485/ijst/2016/v9i12/85910, March 2016 ISSN (Print) : 0974-6846 ISSN
(Online) : 0974-5645
9. Semantic Web Service Selection Based on Service Provider’s Business
Offerings 2015 IJSSST, Vol. 10, No. 2 ISSN: 25 1473-804x Online, 1473-8031
10. Ontology based Comprehensive Architecture for Service Discovery in
Emergency Cloud International Journal of Engineering and Technology (IJET)
2014

33. Thank you