Mobile Cache Memory Optimization using Noise Reduction
DOI:
https://doi.org/10.26438/ijcse/v6i11.925931Keywords:
Noise reduction, web content extraction, caching, pre-fetchingAbstract
Web pages not only contains useful information, but also many features to improve readability and presentation which end up distracting the relevant content as well as occupying more precious memory space. It becomes even more problem while stored in limited mobile cache and prefetch area. While caching and prefetching or when a page is used repeatedly these unnecessary content, called noise such as banner, advertisements, copyright, background images and license information etc. occupy more space, bandwidth while it doesn’t add any value to the user of actual content. Eliminating such noises helps in overall performance improvement of mobile caching, and perfecting. If such noises are not removed, they will become nuisance in web content mining as well. There are many contents which can be identified as noise and there are many techniques to remove them. This paper identifies and removes irrelevant noises in web pages such as background images, search panel, copyright, license information, advertisement. Removing image heavy contents reduces cache memory utilisation, improves performance of content rendering considerably. Care is taken only to remove noises identified and leave the useful contents intact. A brief over view of noise removal and its benefits are discussed in this paper.
References
[1] Yogita K Patel, Nrendasinh Limbad, “Noise Removal from Web Pages for Web Content Mining”, IJAIIE, Vol. 2, Issue 3, pp 2293 – 2299, 2016.
[2] Dauta, S. Paria and D.K. Kole , “Structural Analysis and Regular Expressions Based Noise Elimination from Web Pages for Web Content Mining”, International conference on advances in computing, communications and informatics”, pp 1445 – 1451, 2014.
[3] L. Yi et al., “Eliminating Noisy Information in Web Pages for Data Mining”, Proceedings of 8th ACM SIGKDD international conference on Knowledge discovery and data mining”, 2003.
[4] L. Yi et al., “Web Page Cleaning for Web Mining Though Feature Weighting”, Proceedings of 18th international joint conference on Artificial Intelligence, 2003.
[5] Julius Onyancha and Valentina Plekhanova, “A User Centric Approach Towards Learning Noise in Web Data”, 12th International conference on Intelligent systems and Knowledge engineering, IEEE, 2017.
[6] Roberto Panerai Velloso and Carina F. Doneles, “Automatic Web Page Segmentation and Noise Removal for Structured Extraction Using Tag Path Sequences”, Journal of information and data management, Vol. 4, No. 3, pp 173 – 187, 2013.
[7] Thanda Htwe and Khin Haymar Saw Hla, “Noise Removing from Web Pages Using Neural Network”, IEEE, Vol. 1, pp 281 – 285, 2010.
[8] H.Xiong, P.N. Tan and V. Kuma, “Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution”, Proceedings in 3rd international conference on data mining, IEEE, pp 387 – 394, 2003.
[9] H. Galhardas, D. Floescu, D. Shasha and E. Simon, “An Extensible Data Cleaning Tool”, Proceedings in ACM SIGMOD International conference on Management of data, 2000. V.J. Hodge and J. Austin, “A Survey of Outlier Detection Methodologies”, Artificial Intelligence Rev., Vol. 22, pp 85-126, 2004.
[10] P.N. Tan, M. Steinbach and V. Kumar, “Introduction to Data Mining”, Addison Wesley, 2005.
[11] Angiulli and C. Pizzuti, “Fast Outlier Detection in High Dimensional Spaces”, Proceedings of 6th European conference on Principles of data mining and knowledge discovery, 2002.
[12] E.M. Knorr, R.T. Ng and V. Tucakov “Distance Based Outliers: Algorithms and Applications”, Very large databases, Vol. 8, pp 237 – 253, 2000.
[13] S.D. Bay and M. Schwabacher, “Mining Distance Based Outliers in Near Linear Time With Randomization and a Simple Pruning Rule”, Proceedings in 9th ACM SIGKDD international conference on Knowledge discovery and data mining”, pp 29-38, 2003.
[14] S. Ramaswamy, R. Rastogi and S. Kyuseok, “Efficient Algorithms for Mining Outliers from Large Data Sets”, Proceedings ACM SIGMOD International conference on management of data, 2000,
[15] Hui Xiong, Gaurav Panddey, Michael Steinbach and Vipin Kumar, “Enhancing Data Analysis with Noise Removal”, Transactions on knowledge and data engineering, IEEE, Vol 18, No 3, pp 304 – 319, 2006.
[16] C. Karypis, “Cluto: Software for Clustering High Dimensional Data Sets”, 2005.
[17] M.M. Breunig, H.P. kriegel, T. Ng and J. Sander, “LOF: Identifying Density Based Local Outliers”, Proceedings of ACM SIGMOD International conference on Management of data, 2000.
[18] P.M. Joshi and S. Liu, “Web Document Text and Images Extraction using DOM Analysis and Natural Language Processing”, Proceedings of 9th ACM symposium on Document engineering, pp 218 – 221, 2009.
[19] Hitesh Kumar Azad, Rahul Raj, Rahul Kumar, Harshit Ranjan, Kumar Abhishek and M.P. Sing, “Removal of Noisy Information in Web Pages”, ICTCS, ACM, 2014.
[20] H.R. Parmar and J. Gadge, “Removal of Image Advertisement from Web Page”, International journal of computer applications, Vol. 27, Issue 7, pp 1-5, 2011.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
