Total views : 271
Performance Analysis of Different Classification Algorithms in Information Retrieval through Web Services
Background/Objectives: The web client gets easily lost in the web’s rich hyper structure as the utilization of web is expanding more step by step. The primary point of proprietor of the website is to give the important data in terms of satisfactory QoS (Quality of Service) factors like throughput, response time, accuracy and content availability. From the client point of view, Web Service based QoS Discovery is a multi-criteria decision mechanism that requires knowledge about the service and its QoS description. These clients are not experienced enough to acquire the best selection of web service and trust the QoS information published by the provider. Methods/Analysis: The existing t Model was used with XML based SOAP protocol in order to solve the problem of UDDI registry which holds QoS description. Findings: The new discovery approach is expected to be the solution for contemporary web service discovery problems. A comparative performance analysis of prominent page rank algorithms was made on the basis of metrics like throughput, response time, recall rate and precision rate etc. Simulation Interface has been designed for classification algorithms. The program is developed for the Fuzzy Logic, Naïve Bayes, Neural Network, Linear Discriminant Analysis and Support Vector Machine using MATLAB application Improvements: The experiment revealed the fact that recall and precision rate are the best to predict the Quality of Service (QoS) supported by various E-Commerce web sites like Amazon, Jabong and Shop Clues etc. Detailed performance analysis further concluded that Neural Network could be the best algorithm to rate the service quality of E-Commerce websites.
Fuzzy Classification, Linear Discriminant Analysis, Neural Network, Support Vector, Universal Discovery Description and Integration (UDDI).
- Third International Conference. Florida (USA): Web Services Orlando; 2005. Jul.
- Maxwell. A treatise on electricity and magnetism. 3rd ed. Oxford: Clarendon. 1892; 2:68–73.
- Huhns MN. Agents as Web Services. IEEE Internet Computing. 2002; 6(4):93–5.
- Mulligan G, Gracanin D. A comparison of SOAP and REST implementations of a service based interaction independence middleware framework. Proc IEEE Simulation Conference; 2009. p. 1423–32.
- Tekli JM, et al. SOAP processing performance and enhancement. IEEE Transactions on Services Computing. 2012; 5(3):387–403. *.
- Walsh EA. UDDI, SOAP and WSDL:-The Web Services Specification Reference Book. 1st ed., Pearson Education; 2002.
- Yu JJ, Zhou G. Dynamic web service invocation based on UDDI. Proceedings of the IEEE International Conference on E-Commerce Technology for Dynamic E-Business (CEC-East’04); 2004.
- Choi SW, et al. QoS metrics for evaluating services from the perspective of service providers. Proc of the IEEE International Conference on E-Business Engineering. ACM; 2007. p. 622–5.
- D’Mello DA, Ananthanarayana VS. A QoS model and selection mechanism for QoS-aware web services. Proceedings of the International Conference on Data Management (ICDM2008); 2008.*
- Gouscos G, et al. An approach to modeling web service QoS and provision price. Proceedings of the 1st International Web Services Quality Workshop-WQW, WISE; Rome, Italy. 2003. p. 1–10.
- Menasce DA. QoS issues in web service. IEEE Internet Computing. 2002; 6(6):72–5.
- W3C Working Group. QoS for Web Services: Requirements and possible approaches. 2003. Available from: http:@//www.w3c.or.kr
- Xu Z, et al. Reputation enhanced QoS-based Web Services discovery. IEEE International Conference on Web Services, ICWS: 2007.
- Ni J, et al. A semantic web service-oriented model for E-Commerce. IEEE; 2007.
- Sun C, et al. Comparison of UDDI registry replication strategies. Proceedings of the IEEE International Conference on Web Services (ICWS’04); 2004.
- Rajendran T, Balasubramanie P. Analysis on the study of QoS-aware Web Services discovery. Journal of Computing. 2009. Available from: http://@sites.google.com/site/journalofcomputing/
- Mao, C, et al. Search-based QoS ranking prediction for Web Services in cloud environments. Future Generation Computer Systems. 2015; 50:111-26.
- Khan MW, Abbas E. Differentiating parameters for Selecting Simple Object Access Protocol (SOAP) vs. Representational State Transfer (REST) based architecture. Journal of Advances in Computer Networks. 2015; 3(1):63–6.
- Bartolini C, Bertolino A, Marchetti E, Polini A. Towards automated WSDL-based testing of Web Services. Proceedings of the 6th International Conference on Service-Oriented&Computing; 2008. p.9524–9.
- Pautasso C. REST vs. SOAP: Making the right architectural decision. 1st International SOA Symposium; 2008 Jul.
- Aljazzaf Z. Bootstrapping quality of Web Services. Journal of King Saud University - Computer and Information Sciences. 2015; 27(3):324–35.
- Athanasopoulos M, Kontogiannis K. Extracting REST resource models from procedure-oriented service interfaces. 2015; 100:149–66.
- Zhang J, et al. A service-oriented multimedia componentization model. International Journal of Web Services Research. 2005; 2(1):54–76.
- Han R, et al. WebSplitter: A unified XML framework for multi-device collaborative Web browsing. ACM; 2000. p. 221–30.
- Hussain S, et al. Survey on services composition synthesis model. International Journal of Computer Science. 2013; 10(1):754–63.
- Reddy CRM, Raghavendra Rao RV. QoS of Web service: Survey on performance and scalability. Computer Science and Information Technology. 2013; 3(9):65–73.
- Yu D, et al. Personalized QoS prediction for Web Services using latent factor models. 2014 IEEE International Conference on Services Computing (SCC); 2014.
- Werner C, et al. Compressing SOAP messages by using differential encoding. Proceedings of IEEE International Conference on Web services; San Diego, CA, USA. 2004. p. 540–7.
- Walsh EA. SOAP and WSDL: The Web Services specification. Reference Book 1st ed. Pearson Education; 2002.
- Rahman AFR,&Alam; H, Hartono R. Content extraction from HTML documents. Document Analysis and Recognition Team. BCL Computers Inc; 2002.
- Zhu X, Tan Z. SEO keyword analysis and its application in website editing system. IEEE 8th International on Wireless Communications, Networking and Mobile Computing (WiCOM): 2012 Sep.
- El-Samary A, et al. Applying data mining of fuzzy association rules to network intrusion detection. Proceedings of IEEE Workshop on Information Assurance; 2006.
- Yanchun M. The intrusion detection system based on fuzzy association rules mining. Computer Engineering and Technology. 2010; 7:V7-667–72.
- Chapelle O. Training a Support Vector Machine in the Primal. Neural Computation. 2007 May; 19(5):1155–78.
- Chang CC, Lin CJ. LIBSVM: A library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology. 2011 Apr; 2(3):1–27.
- Kuzmina T, et al. Neural Network control system for a tracked robot. IEEE NW Young Researchers in Electrical and Electronic Engineering Conference;,Russia. 2015. p.0233–5.
- Shaohui L, et al. Neural Network based steganalysis in still images. Proceedings of IEEE ICME; 2003.
- Naseera S, Rajini GK, Amutha Prabha N. A comparative study on CPU load predictions in a computational grid using# Artificial Neural Network Algorithms. Indian Journal of Science and Technology. 2015; 8(35).
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.