Accepted Papers

  • FILESHADER: Entrusted Data Integration Using Hash Server
    Juhyeon Oh and Chae Y.Lee, Korea Advanced Institute of Science and Technology, South Korea
    The importance of security is increasing in a current network system. We have found a big security weakness at the file integration when the people download or upload a file and propose a novel solution how to ensure the security of a file. In particular, hash value can be applied to ensure a file due to a speed and architecture of file transfer. Hash server stores all the hash values which are updated by file provider and client can use these values to entrust file when it downloads. FileShader detects to file changes correctly, and we observed that it did not show big performance degradation. We expect FileShader can be applied current network systems practically, and it can increase a security level of all internet users.
  • COQUEL: A Conceptual Query Language Based on the Entity-Relationship Model
    Rafael Bello and Jorge Lloret, University of Zaragoza, Spain
    As more and more collections of data are available on the Internet, end users but not experts in Computer Science demand easy solutions for retrieving data from these collections. A good solution for these users is the conceptual query languages, which facilitate the composition of queries by means of a graphical interface. In this paper, we present (1) CoQueL, a conceptual query language specified on E/R models and (2) a translation architecture for translating CoQueL queries into languages such as XQuery or SQL..
  • Feature Selection-Model-Based Content Analysis for Combating Web Spam
    Shipra Mittal and Akanksha Juneja, National Institute of Technology, India
    With the increasing growth of Internet and World Wide Web, information retrieval (IR) has attracted much attention in recent years. Quick, accurate and quality information mining is the core concern of successful search companies. Likewise, spammers try to manipulate IR system to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents in search engine result page (SERP). Spammers take advantage of different features of web indexing system for notorious motives. Suitable machine learning approaches can be useful in analysis of spam patterns and automated detection of spam. This paper examines content based features of web documents and discusses the potential of feature selection (FS) in upcoming studies to combat web spam. The objective of feature selection is to select the salient features to improve prediction performance and to understand the underlying data generation techniques. A publically available web data set namely WEBSPAM-UK2007 is used for all evaluations.
  • A Fuzzy Based Semantic Search Engine for Document Retrieval in a Personalized Data Space
    S.V.Manisekaran and J.Sathishkumar, Anna University Regional Campus Coimbatore, India
    Classifying the content oriented search in a personalized document-spaceoften becomes inconvenient and inconsistentdue to the need to reach the actual want of the user. The traditional search tools consider the query as it is and hence imposes on restrictions in collecting the relevant data. This work proposes a fuzzy based content search in which the scoring of content is gathered from inverse document frequency mechanism.The queries are fuzzified and also involved in scoring part of information retrieval to explore the users real component of interest in the query.