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A Framework to Harvest Page Views of Web for Banner Advertising

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Big Data Analytics (BDA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9498))

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Abstract

Online advertising provides an opportunity for product sellers and service providers to reach customers and has become a key factor in the growth of economy. It is a major source of revenue for the major search engine and social networking sites. Search engine, context-specific and banner advertising are the major modes of online advertising. The banner advertisement mode has certain advantages over other modes of advertising. Currently, the number of websites registered comes to a billion. Each day, a typical website receives the number of visitors ranging from hundreds to millions. In a few years, the entire population of the globe is going to be connected to Internet and browse websites. It is possible for a product seller or service provider to reach every potential customer through banner advertising. In this paper, a framework is proposed to harvest the pages views of web by forming the clusters of similar websites. Rather than managing a single website, the publisher manages the aggregated advertising space of a collection of websites. As a result, the advertisement space could be expanded significantly and it will provide the opportunity for increased number of publishers to market the aggregated advertisement space of millions of websites to advertisers for reaching potential customers. It will also help in balancing the management of banner advertising market.

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Authors and Affiliations

  1. Center for Data Engineering, IIIT Hyderabad (IIIT-H), Hyderabad, Telangana, India

    P. Krishna Reddy

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  1. P. Krishna Reddy
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Correspondence to P. Krishna Reddy .

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Editors and Affiliations

  1. University of Delhi, Delhi, India

    Naveen Kumar

  2. University of Delhi, Delhi, India

    Vasudha Bhatnagar

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Reddy, P.K. (2015). A Framework to Harvest Page Views of Web for Banner Advertising. In: Kumar, N., Bhatnagar, V. (eds) Big Data Analytics. BDA 2015. Lecture Notes in Computer Science(), vol 9498. Springer, Cham. https://doi.org/10.1007/978-3-319-27057-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-27057-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27056-2

  • Online ISBN: 978-3-319-27057-9

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