International Journal of Advanced Engineering Research and Science (IJAERS)
Vol-3, Issue-4 , April- 2016] ISSN: 2349-6495
Educational Search Engine: Effective & Scalable Location Based Android Search Engine Using KNN& Removal of Duplicates Using UDD M.Gayathri1, T.Neeharika2, R.Sai Shilpa3, G.Akshaya4 1
Assistant professor, Department of CSE, SCSVMV University, Enathur, Kanchipuram, India 2,3,4 U.G Scholar, Department of CSE, SCSVMV University, Enathur, Kanchipuram, India
Abstract—In recent studies made on the structure and dynamics of the web, it is known that the web is growing in large extent and using high storage, and the dynamics of the web is shifting. In the Existing System, A major problem in education search is that the interactions between the users and search engines are limited by the small form factors of the education devices. As a result, education users tend to submit shorter, hence, more ambiguous queries compared to their web search counterparts. In the Proposed model, user searches on the query, either Area specified (or) user’s location, server retrieves all the information to the user’s PC where ontology was applied. User PC displays all the relevant keywords to the user’s education, so that user selects the exact requirement. Ranking occurs and finally exactly mapped information is produced to the user’s education. In the Modification, We apply UDD algorithm to eliminate the duplication of records which helps to minimize the number of URL listed to the user. Keywords—Search engine, Information retrieval, Location based services, Ontology, Semantic web.
I. INTRODUCTION Now a days there is a tremendous development on information technology in this e-learning plays a major role. To facilitate adaptive and individualized learning, teachers are encouraged to develop adaptive teaching materials for their courses and students. Inaddition, many learning content repositories are built by educational institutes and organizations. The issue of ``content explosion'' is known through the normal search engine. Conventional e-learning systems are stand-alone, which manage only their own learning content. In this kind of system, teaching materials are usually stored in a database, named the Learning Object Repository (LOR). However, when several LORs are built in different sites on the Internet, there exists a need to share learning objects across the Internet. In all the e-learning search engines it will search only the data according to some subjects or courses. Here, we are filtering the data www.ijaers.com
based on user query which will retrieve the optimal result of given query based on education. During the past decade, Grid computing has emerged to support resourcesharing and to overcome the limitations of computing power and storage capacity in conventional computing platforms. Many data-intensive applications, such as highenergy physics, bioinformatics applications, etc., require data file management systems to manage replication, transfer and access of data. In fact, more and more effort has gone into the field of e-Learning, using data mining technologies in the context of e-Learning. When content is stored and shared on database, there exists a great demand to find desirable teaching materials from multiple repositories in databases. The problem is similar to information retrieval, which has been widely investigated in the past. A straightforward approach is to apply existing information retrieval methods on the basis of education. Learning content is based on three types of information: text, metadata and structural information, which is different from conventional documents and web pages. On the other hand, data grids are implemented in a layered manner, so applications have to consider collaborative aspects with underlying components to improve their performance. II. LITERATURE REVIEW PAPER 1: Dynamic Provable Data Possession As storage-outsourcing services and resource-sharing networks have become popular, the problem of efficiently proving the integrity of data stored at untrusted servers has received increased attention. In the provable data possession (PDP) model, the client pre-processes the data and then sends it to an untrusted server for storage, while keeping a small amount of meta-data. The client later asks the server to prove that the stored data has not been tampered with or deleted (without downloading the actual data). However, the original PDP scheme applies only to static (or append-only) files. We present a definitional framework and efficient constructions for dynamic provable data possession (DPDP), which extends the PDP model to support provable updates to stored data. We use Page | 95
International Journal of Advanced Engineering Research and Science (IJAERS) a new version of authenticated dictionaries based on rank information. The price of dynamic updates is a performance change from O(1) to O(log n) (or O(nǫlog n)), for a file consisting of n blocks, while maintaining the same (or better, respectively) probability of misbehaviour detection. Our experiments show that this slowdown is very low in practice (e.g., 415KB proof size and 30ms computational overhead for a 1GB file). We also show how to apply our DPDP scheme to outsourced file systems and version control systems (e.g., CVS). PAPER 2: Secure Cloud Data Storage Services Toward Publicly Auditable Cloud computing is the long dreamed vision of computing as a utility, where data owners can remotely store their data in the cloud to enjoy on-demand highquality applications and services from a shared pool of configurable computing resources. While data outsourcing relieves the owners of the burden of local data storage and maintenance, it also eliminates their physical control of storage dependability and security, which traditionally has been expected by both enterprises and individuals with high service-level requirements. In order to facilitate rapid deployment of cloud data storage service and regain security assurances with outsourced data dependability, efficient methods that enable ondemand data correctness verification on behalf of cloud data owners have to be designed. In this article we propose that publicly auditable cloud data storage is able to help this nascent cloud economy become fully established. With public audit ability, a trusted entity with expertise and capabilities data owners do not possess can be delegated as an external audit party to assess the risk of outsourced data when needed. Such an auditing service not only helps save data owners’ computation resources but also provides a transparent yet cost-effective method for data owners to gain trust in the cloud. We describe approaches and system requirements that should be brought into consideration, and outline challenges that need to be resolved for such a publicly auditable secure cloud storage service to become a reality. III. EXISTING SYSTEM A major problem in education search is that the interactions between the users and search engines are limited by the small form factors of the education devices. As a result, education users tend to submit shorter, hence, more ambiguous queries compared to their web search counterparts. IV. PROPOSED SYSTEM In the Proposed Model, users search’s on the web for query, either Area specified (or) user’s location, server retrieves all the information to the user’s Pc where www.ijaers.com
Vol-3, Issue-4 , April- 2016] ISSN: 2349-6495
ontology is applied. User Pc displays all the relevant keywords to the user’s education, so that user selects the exact requirement. Ranking occurs and finally exactly mapped information is produced to the user’s education. V. ARCHITECHTURE Components in proposed architecture are Education user: An Android education client is an application that access a service made available by a server. The server is often (but not always) on another computer, in which case the client accesses the service by way of a network. The term was first applied to devices that were not capable of running their own stand-alone programs, but could interact with remote computers via a network. To send the request to the server, the users have to be a registered person in the server. The user have to submit their user name password and another details to the server during the registration phase. All this information is stored in the database via server for future purpose.
Fig 1: Architecture Diagram Main server: A server is a computer program running to serve the requests of other programs, the "clients". Thus, the "server" performs some computational task on behalf of "clients". The clients either run on the same computer or connect through the network. Here the Server acts as the main resource for the client. Server is responsible for maintaining all the client information. So the server will process the user’s request and get the concerned data from the database. User query process: Once the user successfully signed in into the server, the user is requested to option to search their data on Query based OR Location based. Once the user chooses the query based, the data link will displayed on the query based. If the user, chooses the location based, the data Page | 96
International Journal of Advanced Engineering Research and Science (IJAERS) will displayed in location based. To get the data via location based, the user’s Education GPS will be used.
VI.
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RESULTS
Fig 3: Home page of Education Search Engine
Fig 2: The query process The major components are user interaction, ranking, and evaluation. The user interaction component provides the interface between the person doing the searching and the search engine. One task for this component is accepting the user’s query and transforming it into index terms. Another task is to take the ranked list of documents from the search engine and organize it into the results shown to the user. This includes, for example, generating the snippets used to summarize documents. The document data store is one of the sources of information used in generating the results. Finally, this component also provides a range of techniques for refining the query so that it better represents the information need. The ranking component is the core of the search engine. It takes the transformed query from the user interaction component and generates a ranked list of documents using scores based on a retrieval model. Ranking must be both efficient, since many queries may need to be processed in a short time, and effective, since the quality of the ranking determines whether the search engine accomplishes the goal of finding relevant information. The efficiency of ranking depends on the indexes, and the effectiveness depends on the retrieval model. The task of the evaluation component is to measure and monitor effectiveness and efficiency. An important part of that is to record and analyse user behaviour using log data. The results of evaluation are used to tune and improve the ranking component. Most of the evaluation component is not part of the online search engine, apart from logging user and system data. Evaluation is primarily an offline activity, but it is a critical part of any search application.
Vol-3, Issue-4 , April- 2016] ISSN: 2349-6495
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Fig 4: Result from Normal Search Engine
Fig 5: Result from Education Search Engine VII. CONCLUSION We proposed a model to extract and learn a user’s content and location preferences based on the user’s click through. To adapt to the user mobility, we incorporated the user’s GPS locations in the personalization process.
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International Journal of Advanced Engineering Research and Science (IJAERS)
Vol-3, Issue-4 , April- 2016] ISSN: 2349-6495
We observed that GPS locations help to improve retrieval effectiveness, especially for location queries. VIII. FUTURE ENHANCEMENT In future work we will try to search semantically related to education. Not only with text filtering we will also implement image filtering and video filtering based on education. In addition, it is a promising way to use expert system technologies to facilitate the search process. Also, we plan to generalize this approach to include domains other than educational applications. REFERENCES [1] http://www.cse.ust.hk/faculty/dlee/tkdepmse/appendix.pdf.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. [2] National geospatial. http://earth-info.nga.mil/. [3] svmlight. http://svmlight.joachims.org/. [4] World gazetteer. http://www.world-gazetteer.com/. [5] E. Agichtein, E. Brill, and S. Dumais, “Improving web search ranking by incorporating user behavior information,” in Proc. of ACM SIGIR Conference, 2006. [6] E. Agichtein, E. Brill, S. Dumais, and R. Ragno, “Learning user interaction models for predicting web search result preferences,” in Proc. of ACM SIGIR Conference, 2006. [7] Y.-Y. Chen, T. Suel, and A. Markowetz, “Efficient query processing in geographic web search engines,” in Proc. of ACM SIGIR Conference, 2006. [8] K. W. Church, W. Gale, P. Hanks, and D. Hindle, “Using statistics in lexical analysis,” Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon, 1991.
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