A recommendation framework for remote sensing images by spatial relation analysis

The large amount of available Web data sources is an important opportunity for Web users and also for various data-intensive Web applications. Nevertheless, the selection of the most relevant data sources and thus of high quality information is still a challenging issue. This paper proposes an approach for data source selection that is based on the notion of reputation of the data sources. The data quality literature defines reputation as a multi-dimensional quality attribute that measures the trustworthiness and importance of an information source.

Enhancing the Selection of Web Sources: A Reputation Based Approach

Searching becomes a normal behavior of our life. Billions of users communicate with search engines daily. They are checking links of results, click on the ads, spend time on pages to restructure their queries and perform other actions. These interactions may concern some of the valuable source of information for tuning the content. There are massive web pages on the internet and search engines also have a test to find the best ranked pages. This paper provides a survey on different ranking algorithms such as link based ranking, content based ranking and usage based ranking and presents a comparative analysis on performance of these algorithms.

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Survey on Different Ranking Algorithms Along With Their Approaches

The paper compares Google’s ranking with the ranking obtained by means of a multi-dimensional source reputation index. The data quality literature defines reputation as a dimension of information quality that measures the trustworthiness and importance of an information source. Reputation is recognized as a multidimensional quality attribute. The variables that affect the overall reputation of an information source are related to the institutional clout of the source, to the relevance of the source in a given context, and to the general quality of the source’s information content. We have defined a set of variables measuring the reputation of Web information sources along these dimensions. These variables have been empirically assessed for the top 20 sources identified by Google as a response to 100 queries in the tourism domain. Then, we have compared Google’s ranking and the ranking obtained along each reputation variable for all queries. Results show that the assessment of reputation represents a tangible aid to the selection of information sources.

Reputation-based Selection of Web Information Sources.

Summary In this paper, we will discuss on the strategic issues of exploring, harvesting and integrating the Deep Web. We will then develop a novel interdisciplinary stochastic model for a Deep Web Search Engine which can detect and rank the contents optimally. Our efforts aim at opening up to users by building Generator. On this information grand voyage, the Generator will address the challenges of exploring, harvesting and integrating of the Deep Web. First, to make the Web systematically accessible: our Generator will focus on the discovery, modeling and structuring of databases on the Web to develop a search engine, in order to help users find sources useful for their information need. Second, to make the Web uniformly usable: the Generator will help users to make optimal choice of keywords. Based on these insights, we design a stochastic model and employ an interdisciplinary approach consisting stochastic and optimization techniques. In addition, we consider three types of keywords: text-based, image-based and hybrid-based. Experimental and simulation results are given for illustrations.

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A Novel Interdisciplinary Approach to Deep Web Search Engine

A search engine is computer software that is continually modified to avail of the latest technologies in order to provide improved search results. Each search engine does the same function of collecting, organizing, indexing and serving results in its own unique way, thus employing various algorithms and techniques, which are their trade secrets. In short, the functions of a search engine can be categorized into the following broad areas: First, crawl the Web and locate all Web pages. Second, index the data. Third, rate the importance of each page in the database, so that when a user does a search and the subset of pages in the database with the desired information has been found, the more important pages can be presented first. The paper throws the light on evolution of search engine's Web page ranking technique that started from the development of Google's pagerank and continues to expand in the form of other techniques. The paper also highlights the shortcomings of the most popular technique. Also, various alternatives to those flaws are discussed. This paper focuses on the current prevailing ranking techniques and tries to probe the conditions under which they can give more benefit. Also the various techniques are discussed in detail to present the flaws in this paper, and finds a way to achieve the ideal technique.

S. Gupta

Papers

Contrast of link based web ranking techniques