Software Engineering

D. Saraswathi ¹, A. Vijaya Kathiravan ²

Affiliations
1 K.S. Rangasamy College of Arts & Science, Tiruchengode-637215, Namakkal, TN, IN
2 Government Arts College, Salem-07, TN, IN

Abstract

An Impact Factor is one measure of the relative importance of a journal, individual article or scientist to science and social science literature and research. Each index or database used to create an impact factor uses a different methodology and produces slightly different results, revealing the importance of using several sources to judge the true impact of a journal's or scientist’s work. In the web environment, impact factor is measured through the number of hyperlinks counts and number of WebPages. The concept of self-citation is replaced by self-links, i.e., the links within the websites and citation is replaced by in-links, i.e., the links coming outside the websites. As we know, WIF is the logical sum of external and self-link WebPages divided by number of web pages found on that particular websites. There are number of ways to find the impact of journal, paper, and Web sites etc. The proposed system is going to find impact factor of E-books by using EigenFactor. This system is used to measure the quality of E-books. Most of the E-books are downloaded from the Web or required pages can read from the Web site itself. This was done by means of a citation analysis and a reader survey. For the citation analysis, impact factor, citing half-life, number of references per article, and the rate of self-references of a periodical were used as indicators. Webometric data have been collected through Yahoo! And Google search engines using special query syntax.

Keywords

Scientometrics, Webometrics, Cybermetrics, Bibliometrics, Impact Factor, EigenFactor, UCINET.

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A. Vijaya Kathiravan ¹, M. Reka ¹

Affiliations
1 Dept of M.C.A. in K.S. Rangasamy College of Technology, Tiruchengode-627 315, IN

Abstract

With the growing Tamil interest and Internet, the amount of Tamil data doubles every 12-14 months and will increase even more dramatically in the coming year. With an enormous amount of Tamil data stored in web pages, it is increasingly important to develop powerful tools for analysis of such Tamil data and mining interesting patterns from it. There is a strong interest in employing methods of data mining to generate models of Tamil related web pages forming web communities. Web community refers collection of web pages of similar interest implicitly or explicitly. This paper proposes a new initiative for forming Tamil web communities with concise introduction about web community mining. The main intention of this paper is to employ web community mining technique for providing better results in search engines and to visualize the search engine results as Tamil web communities using a suitable visualization tool. This paper exploits visualization, web community mining and natural language processing (NLP) techniques. Visualization is the graphical presentation of information, with the goal of providing the viewer with a qualitative understanding of the information contents. This paper focuses on selecting the appropriate visualization tool best suited for displaying search engine results using visualization techniques. Various visualization techniques are also described in this paper. This community mining will yield benefits to all Tamil lovers,who want to be well-versed in a Tamil domain of his own interest. Tamil research publications and literatures in Tamil are grouped using bibliometric analysis. By forming people communities (i.e., people belonging to similar interest) using social network analysis,the domain knowledge in Tamil can be shared. Hence, web community mining may play an important role in forming Tamil Web Communities for gathering Tamil resources and documents of similar interest from the ocean of web very easily.

Keywords

Visualization, Search Engine, Web Community Mining, Information Retrieval, Tamil Communities, Social Network Analysis, Bibliometric Analysis, Community Mining, and Natural Language Processing (NLP), Tree Graph, Map Graph, Bi-Partite Graph.

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