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Effective Semantic Web Service Composition Framework Based on QoS

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

Published in: Technology
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Effective Semantic Web Service Composition Framework Based on QoS

  1. 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. 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. 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. 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. 5. Functional and non-functional properties • Functional properties: 1. Input 2. Output 3. Precondition 4. Effects • Functional properties (QoS):
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18. Problem Constraints • Initial knowledge constraints • Goal knowledge constraints • Web services invocation constraints • Non-concurrency constraints
  19. 19. Semantic Representation in Cloud Services
  20. 20. Cloud Service Ontology
  21. 21. Cloud Provider Ontology - Open Stack
  22. 22. OWL-S Annotation- Open Stack
  23. 23. Resources Configurations- Open Stack
  24. 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. 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. 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. 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. 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. 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. 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. 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. 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. 33. Thank you

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