Qualification: 
Ph.D, MCA, MPhil
subbulakshmis@am.amrita.edu

Dr. S. Subbulakshmi is currently working as Assistant Professor (Sr. Gr) Department of Computer Science Applications in Amrita School of Engineering, Amritapuri Campus. She received her Ph. D. in Computer Science for the topic “Architecture for Web Services to enhance the quality of service for Information Delivery” done under the guidance of Dr. K. Ramar, Principal, Einstein College of Engineering, Tirunelveli.

Her area of research includes Semantic Web Services, Ontology based Web Service Composition, QoS Prediction of Web Services.

Publications

Publication Type: Journal Article

Year of Conference Title

2019

S. Subbulakshmi, Ramar, K., Omanakuttan, A., and Sasidharan, A., “Automated analytical model for content based selection of web services”, Communications in Computer and Information Science, vol. 968, pp. 309-321, 2019.[Abstract]


There are various inbound web services which prescribe services to clients. Specialists are more engaged in making framework for proposal of web service (WS) which limit the intricacy of selection process and improve the quality of service (QOS) suggestion. Our work implements a framework which recommends web services using an analytical model based on the contextual information provided by the service providers. This system helps users obtain high quality service automatically. Adaptive work performs feature reduction, similarity and ranking of WS. The important feature reduction process helps identify attribute values with maximum accuracy which results in proper evaluation of data. Efficient selection of WS for service composition requires better methods which properly calculate the similar values. A similarity helps to identify the closest services as per the requirement in the process of service composition. Ultimately, the system automatically selects the set of web services with highest similarity scores from the optimized set of web service description. © 2019, Springer Nature Singapore Pte Ltd.

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2018

S. Subbulakshmi, Rahul Varma U., and Nair, S., “PLING Adaptive Opportunity Checking of Web Service with Recommendation System”, International Journal of Pure and Applied Mathematics, vol. 119, 16 vol., pp. 247-253, 2018.[Abstract]


In the recent advancement of e-commerce, most of the populace in the world are using the World Wide Web for the retrieval of information and to perform various transactions through internet. This has led to the increase in the complexity of information retrieval and thereby creating hurdles on efficient and fast filtering of information. To overcome the above problem, and to help the online readers to handle huge volume of data, a new method of information filtering has to be created. In this paper we present a new hybrid filtering approach which is the combination of collaborative and content based filtering. Collaborative Filtering is the method of matching people who have similar interest, which is done by collecting desirous information from many users using Pearson Correlation Coefficient. Content Based Filtering uses Cosine Similarity for analyzing the content of the user profile and the past browsing history maintained for each user. We present design of Adaptive Web Service Selection Framework that makes use of automated approach that systematically integrates all available training information such as past user, user similarity ratings as well content of user-paper rating, paper-user rating and user browsing history. The vital solution of our approach is simple relative frequency percentage bar to illustrate the combined approach which creates the most accurate web services recommendation to the individual user. The system is able to absorb the result of both filtering technique of information theories and it can be adopted for better and efficient recommendation. The results of this system reveals the fact that is able to dynamically offer recommended services based on end user’s interest.

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Publication Type: Conference Paper

Year of Conference Title

2018

S. Subbulakshmi, Ramar, K., Krishna, V. C. K., and Sanjeev, S., “Optimized QoS Prediction of Web Service using Genetic Algorithm and Multiple QoS Aspects”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018.[Abstract]


In the present Internet era, with the seemingly insatiable growth in applications based on web services (WS), it is an arduous task for an user to select the foremost web service among a large number of competing services. Prediction of quality of service (QoS) ratings of WS helps to identify optimal WS. The ratings are appraised by an evaluation of different QoS factors. However, in the real world, missing values are often major problem in datasets of QoS factors, as they lead to imprecise prediction of QoS rating of a given web service. We present here a system designed for imputing the missing values in QoS datasets, by clustering, similarity checking and optimization techniques. The optimization of QoS values helps to obtain the more accurate dataset. Finally, QoS prediction of WS is calculated using imputed dataset. It helps to ascertain the quality and ranking of different WS. Experimental results show that the proposed method predicts web service QoS values more accurately and using these complete dataset an optimal web service is predicted/suggested to the user.

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2018

S. Subbulakshmi, Ramar, K., Shaji, A., and Prakash, P., “Web Service Recommendation Based on Semantic Analysis of Web Service Specification and Enhanced Collaborative Filtering”, in Intelligent Systems Technologies and Applications, Cham, 2018.[Abstract]


With growing momentousness of Internet applications, digital world is overwhelmed with huge number of web services. To ease the job of selecting relevant WS in service composition process, recommendation system of Web Services is designed. It uses semantic analysis of WS along with enhanced collaborative filtering. Ontology based Semantic Analysis performed using Tversky Content Similarity Measure helps to identify most similar functionally relevant WS. The collaborative filtering process uses DBSCAN clustering and PCC similarity to identify highly collaborative WS, based on ratings given by experienced users. To overcome the existence of sparse data in WS ratings and to enhance filtering process, SVM Regression is implemented before collaborative filtering. Relative frequency method is applied to amalgamate collaborative and sematic similarity values of WS. The methodology is proved to produce more realistic, accurate and efficient WS recommendation. Future focus may be towards knowledge based filtering with real world contextual information.

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2016

S. Subbulakshmi, Ramar, K., Renjitha, R., and Sreedevi, T. U., “Implementation of Adaptive Framework and WS Ontology for Improving QoS in Recommendation of WS”, in The International Symposium on Intelligent Systems Technologies and Applications, Intelligent Systems Technologies and Applications 2016, Cham, 2016.[Abstract]


With the advent of more users accessing internet for information retrieval, researchers are more focused in creating system for recommendation of web service(WS) which minimize the complexity of selection process and optimize the quality of recommendation. This paper implements a framework for recommendation of personalized WS coupled with the quality optimization, using the quality features available in WS Ontology. It helps users to acquire the best recommendation by consuming the contextual information and the quality of WS. Adaptive framework performs i) the retrieval of context information ii) calculation of similarity between users preferences and WS features, similarity between preferred WS with other WS specifications iii) collaboration of web service ratings provided by current user and other users. Finally, WS quality features are considered for computing the Quality of Service. The turnout of recommendation reveals the selection of highly reliable web services, as credibility is used for QoS predication.

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Publication Type: Conference Proceedings

Year of Conference Title

2016

S. Subbulakshmi, Ramar, Ka, Krishnan, RaArya, and Divya, Sb, “Web services QoS prediction basedon dynamic non-functional quality factors and WS-security policy specification of web service”, Second International Conference on Computer paradigms(ICCP 2016), International Journal of Control Theory and Applications, vol. 9. International Science Press., pp. 4591-4601, 2016.[Abstract]


Web service are basically software components that support interoperable interaction between machines over a network. With booming number of WS, it's difficult for users to identify the best quality services. Existing researchers focus mainly on selection of WS based on the functional requirements. This paper proposes a system to select an optimal WS by predicting its QoS value based on dynamic non-functional quality factors response time, throughput and the static factor security of the WS. Prediction result can be used in recommendation systems to select services with optimal QoS performance among a large volume of service candidates. Users and web services are clustered to make prediction of response time and throughput of WS. Security specifications of the web service and their vulnerabilities are used for prediction of security factor of the WS. Finally, QoS of the WS is predicted by aggregation of the predicted quality values for security, response time and throughput. The QoS prediction of the system reveals optimal results as it takes both dynamic and static quality factors of WS. © International Science Press.

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