Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Sureshan, S.

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    O3 - A webpage preprocessing tool
    (SciTePress, 2015) Senthil, K.; Bhat, K.S.; Jamadagni, N.; Sureshan, S.; Prasad, G.
    One of the prime factors for the success of the internet is determined by the time taken to load a web page. Even a difference of a few hundred milliseconds in the response time will largely affect the number of users of a web page to shift from one to the other. So, in the commercial market, providing quick service to the users is of utmost importance in remaining ahead of competitors. In this paper, we mainly address this issue by applying various optimization techniques at the front-end to improve the user experience by reducing the load time of the web pages. Though the overall optimization is purely web page-dependent, the optimization techniques not only reduce the time taken to load the page, but also reduce the load on the server.
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    Mining closed colossal frequent patterns from high-dimensional dataset: Serial versus parallel framework
    (Springer Verlag service@springer.de, 2018) Sureshan, S.; Penumacha, A.; Jain, S.; Vanahalli, M.; Patil, N.
    Mining colossal patterns is one of the budding fields with a lot of applications, especially in the field of bioinformatics and genetics. Gene sequences contain inherent information. Mining colossal patterns in such sequences can further help in their study and improve prediction accuracy. The increase in average transaction length reduces the efficiency and effectiveness of existing closed frequent pattern mining algorithm. The traditional algorithms expend most of the running time in mining huge amount of minute and midsize patterns which do not enclose valuable information. The recent research focused on mining large cardinality patterns called as colossal patterns which possess valuable information. A novel parallel algorithm has been proposed to extract the closed colossal frequent patterns from high-dimensional datasets. The algorithm has been implemented on Hadoop framework to exploit its inherent distributed parallelism using MapReduce programming model. The experiment results highlight that the proposed parallel algorithm on Hadoop framework gives an efficient performance in terms of execution time compared to the existing algorithms. © Springer Nature Singapore Pte Ltd. 2018.