Abstract
A Web cache content mining is a very important part in analyzing and filtering the internet contents. Essentially, a log data will be used to identify either to cache or not to cache Web contents in a cache server. This data contains dissimilar elements consisting of URL, size, retrieval time, number of hits and other elements for Web contents. In this chapter, we propose a new method and analyses of our cache server data using social network analysis (SNA); and make a number of statistic measurements to reveal the hidden information on E-Learning@UTM (EL) and Boston University (BU) logs dataset. The log dataset was extracted by particular queries, and it was displayed as a connected graph and clustered based on a similarity of characteristics. Later, the statistical properties of dataset network were computed, including speed and complexity. The result shows the SNA important behaviors: data localization in a separate position; and centralized data in a single position approve that the concentration of data to one node. These behaviors are driven by complexity of the dataset and network nodes structure of the chosen log dataset.
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Acknowledgments
The first author would like to thank the Ministry of Higher Education (MOHE) for her scholarship. The authors also would like to thank Research Management Centre (RMC), Human Capital Development Unit and Soft Computing Research Group (SCRG), K-Economy Research Alliance, Universiti Teknologi Malaysia (UTM) for their overwhelming cooperation. We are sincerely thankful to Ahmad Hoirul Basori for providing helpful and excellent support to realize this research.
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Sulaiman, S., Shamsuddin, S.M., Abraham, A. (2012). Implementation of Social Network Analysis for Web Cache Content Mining Visualization. In: Abraham, A. (eds) Computational Social Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4054-2_14
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DOI: https://doi.org/10.1007/978-1-4471-4054-2_14
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