Personalized Search Engine for Efficient Service Discovery
Personalized Search Engine for Efficient Service Discovery K. Tamilarasi1, Dr M. Ramakrishnan2 1
Research Scholar, Department of Computer Science and Engineering, Sathyabama University, Chennai. Tamilnadu 2
Professor and Chairperson, School of Information Technology, Madurai Kamaraj University, Madurai, Tamilnadu
[email protected] Abstract: Web service discovery plays an integral part of today’s internet and finding the relevant and expected results is a time consuming process. Users fed up with the search results when they get enormous results. Also there is chance of divergence of user behavior due to the search results which may end up in great social distraction. To address these issues, the search engines may be personalized by getting one time user information by registering their focus areas, fields of interest, age, locality and other personal information and may be used for getting relevant results during subsequent usages. This paper aims in providing a user interface for search engines to facilitate users with more personalization and accurate search results. They need to spend time only once to register them and the same can be edited or updated as the user behavior changes. This may be optional and may be used if personalization and customization is needed. Otherwise they can end up with regular results. The system has been tested with nominal number of web services and the results of response time and relevancy are promising. Keywords- Web service discovery, personalization, customization, search engine, UDDI, service registry.
I. INTRODUCTION
discovery based on indexing UDDI[3]. Recently research is focused towards group recommendations rather than individual recommendations. In order to personalize, the system should be able to distinguish between different users or groups of users. This process is called user profiling and its objective is the creation of an information base that contains the preferences [4], characteristics, and activities of the users. In the web domain and especially in e-commerce, user profiling has been developed significantly because Internet technologies provide easier means of collecting information about the users of web site, which in the case of e-business sites are potential for customers [5]. The clustering process is an important step in establishing user profiles. User profiling on the web consists of studying important characteristics of the web visitors [6].
Web Services are important components of today’s II. PROPOSED SYSTEM internet and major task is to satisfy the end users of the system by enabling the unique and relevant The proposed model IUISE intelligent user interface discovery in immediate time [1]. To address this for search engine gets the details about the user issue, this paper proposes personalized web service interest and his/her browsing behavior. When the discovery recommendation framework by user searches subsequently the IUISE may opt for incorporating customization and personalization customization and personalization. If the user selects during Web service discovery. Also the discovery the options they may end up only with the previous processes of web services are integrated with search and relevant results within the short response time. engine to fulfill the consumer requirements. Also ranking of web services is done by means of Performance of the recommended system is studied QoS constraints of web services. Proposed and evaluated using real world services and framework of intelligent user interface for search considered as a vital task which is carried out to engine is depicted in Fig 1, IUISE framework improve the social networking and user satisfaction. includes the following components. Today’s world expects every individual should be uniquely identified and given importance [2]. For any data, everyone is relying on today’s internet and irrespective of the person who is browsing the internet the discovery results are similar and there is no difference with respect to age, locality and any other criteria. To suit the needs of the current scenario, this paper proposes architecture of Fig 1. Architecture Diagram recommendation framework for personalized ACS – International Journal in Computational Intelligence, Volume – 07, Issue – 02 October 2015 Page No: 25
K. Tamilarasi1, Dr M. Ramakrishnan2 III. IUISE COMPONENTS
In this user interface, users have the option of whether to include the customization and personalization in their discovery process. Personalizer : In this module, user personalized database is constructed by analyzing the user clicks, navigational patterns, age, locality, ratings, time spent during their previous visits etc and passed as keywords for querying from the service registry. Customizer: In this module, user is able to register their profile which consists of their personal and professional details which is used in the Personalizer module of the intelligent user interface. Ranker: This module ensures the reliable discovery of web services by checking the integrity and QoS constraints. This ranking is used for indexing the services in IUDDI. Quantified web service discovery is promised with this module. Recommender: Sensitive recommendation of web services to the user by mean of user preference database. Personalization focus on inferring the needs of the requestor based on current and prior browsing behavior of end user. Server logs are the source of data which are splendid but should be manipulated properly from which preferences and customs of user are derived. IUDDI: Indexing mechanism is introduced in UDDI service registry to enhance the searching capability. Services stored in the registry is indexed and updated after every new registration of services. This mechanism ensures that the relevant web services are fetched to the user using index database in short response time. To avoid the huge storage of user profile, profile database should be examined for last visit and after some time the profile may be removed from the index database.
If selected personalization Searching is considered using customized user profiles in web logs Personalization - Constructing of user preference database Grouping of business and business services Indexing of web services – Index database User enters input request for web service; for each input Input goes to Search Engine; Search Engine passes request to Personalizer; Personalizer search from user preference database DUP If found Results are returned else Search Engine passes request to Indexer; IB and IS Indexer search from index database based on classification and indexing; Search Engine adds references of matched services in user preference database DUP through Personalizer for future reference; Return results to user; else Return no service found; V. IMPLEMENTATION
The proposed system is implemented using JAVA, J2EE, Net beans Framework. We have used Word net java API for synonyms. To evaluate the effectiveness of the proposed method, performance is measured using two factors like precision and efficiency. Precision means number of relevant services discovered to the total number of services discovered. Efficiency of the system is calculated using the discovery time. Precision and Response Time of the services discovered are measured for varying number of recommended services. Implementation is done through the web application and results are obtained as shown in the following Figure.
IV. ALGORITHM Algorithm : Efficient Web Services Discovery Input: Request for Web service Output: Desired Relevant Service Services published in UDDI registries; Customization – user opts for customization. Personalization – user opts for personalization If selected customization Provision for changing user profile else
Figure 2. Precision Analysis
ACS – International Journal in Computational Intelligence, Volume – 07, Issue – 02 October 2015 Page No: 26
Personalized Search Engine for Efficient Service Discovery REFERENCES [1] [2] [3] [4] [5] [6] [7]
Figure 3. Efficiency Analysis [8]
VI. CONCLUSION
As customization and personalization process plays a vital role in e-business, this work aims to integrate the process in service discovery which is today’s important activity. In future this methodology may be further enhanced with fuzzy intelligent techniques for prompt and accurate web service discovery.
[9]
Discovering Web Services in Search Engines. Eyhab Al-Masri and Qusay H. Mahmoud • University of Guelph. A QoS-aware Selection Model for Semantic Web Services Xia Wang1 Tomas Vitvar1 Mick Kerrigan and Ioan Toma. Towards Semantic Web Services Discovery with QoS Support using Specific Ontologies Haihua Li, Xiaoyong Du, Xuan Tian. Investigating Web Services on the World Wide Web Eyhab Al-Masri and Qusay H. Mahmoud(2008) A QoS-aware Model for Web Services Discovery Gang YE, Chanle WU, Jun YUE, Shi CHENG, Chanle WU WSCE: A Crawler Engine for Large-Scale Discovery of Web Services Eyhab Al-Masri and Qusay H.Mahmoud. Tamilarasi, K. and M. Ramakrishnan, 2012. Design and Development of an Enhanced UDDI for Efficient Discovery of Web Services. Advances in Communication, Network, and Computing Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Vol: 108, pp: 109-114. Tamilarasi, K., M. Ramakrishnan, 2012. Design of an intelligent search engine-based UDDI for web service discovery, international conference on recent trends in information technology, pp: 520-525. WSDL and UDDI Extensions for Version Support in Web Services, M.B. Juric, A. Sasa, B. Brumen, I. Rozman J. of Systems and Software, 82 (2009), pp. 1326–1343
**** IJCI-2K15-351****
ACS – International Journal in Computational Intelligence, Volume – 07, Issue – 02 October 2015 Page No: 27
