International Journal Of Engineering Technology And Management Sciences

AUTHOR(S)

Santosh Kumar Jha

DOI: https://doi.org/10.46647/ijetms.2023.v07i01.017

ABSTRACT
Web has immense potentials to grow with new technologies and flourish with new opportunities in almost every sphere of human life. The internet grew by WWW and explored with e-business and social network. The Web is known and participating in mining of various kinds of data ranging from users views and patterns to Bitcoin applications. Web mining includes different set and types of data and extract useful information and from various sources of web and gain knowledge using applicable data mining technique on dataset. Web mining types broadly categorised in three main areas, web uses mining, web content mining, and web structured mining. Each category of web mining is involved to handle issues of heterogeneous behaviour of web data. All three technique and applications are highly required of high end architectures which can facilitate infrastructures and support for all required criteria. Cloud computing is an emerging technology and blowing with intensive support for varieties of applications. The techniques and applications of Web usage mining are extremely demanded in cloud computing. Cloud computing allow these technique to retrieve relevant and useful data through virtually integrated mode of data warehouse. It helps the users to reduce cost and infrastructure for implementation. This paper presents methodologies of web mining using Cloud Computing technology and its prospects.

Page No: 98 - 104

References:

1. Web Data Mining Exploring Hyperlinks, Contents, and Usages Data By Bing Liu Published by Springer
2. Georges Dupret, Mounia Lalmas, 2013, Absence Time and User Engagement: Evaluating Ranking Functions,WSDM’13, February 4–8, 2013, Rome, Italy
3. Hicham Snoussi, Laurent Magnin, and Jian-Yun Nie, Heterogeneous Web Data Extraction using Ontology
4. R. Cooley, B. Mobasher, and J. Srivastava, “Web mining: information and pattern discovery on the World Wide Web,” in Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, CA, 1997, pp. 558-567.
5. ”A Framework for Personal Web Usage Mining”.
6. Kaikala Anjani Sravanthi1, “Web Mining Using Cloud Computing” , ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 4, April 2013.
7. Web mining, “www.wikipedia.com”, 10-02-2015.
8. Rajni Pamnani, Pramila Chawan Department of computer technology, VJTIUniversity, Mumbai “Web Usage Mining: A Research Area in Web Mining”.
9. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Discovery and evaluation of aggregate usage profiles for we personalization. Data Mining and Knowledge Discovery, 6(1):61{82, January 2002.
10. Faten Khalil, Jiuyong Li and Hua Wang ―A Framework of Combining Markov Model with Association Rules for Predicting Web Page Accesses‖ ,Proc. Fifth Australasian Data Mining Conference (AusDM2006), CRPIT Volume 61,177-184.
11. Robert Grossman , Yunhong Gu, “Data mining using high performance data clouds: experimental studies using sector and sphere”, Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008
12. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White. Visualization of navigation patterns on a web site using modelbased clustering. In In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 280{284, Boston, Massachusetts, 2000.
13. Zhu, W., & Lee, C. (2014). A new approach to web data mining based on cloud computing. Journal of Computing Science and Engineering, 8(4), 181–186. doi:10.5626/jcse.2014.8.4.181
14. “MapReduce.” Wikipedia. N.p.: Wikimedia Foundation, 11 Jan. 2017. Web. 2   Jan. 2017.
15. Divestopedia, and Securities Institute. What is MapReduce? - definition from Techopedia. Techopedia.com, 2017. Web. 2 Jan. 2017.
16. Posted, and Margaret Rouse. What is MapReduce? - definition from WhatIs.com. SearchCloud Computing, 25 June 2014. Web. 2 Jan. 2017.
17. Hornung, T., Przyjaciel-Zablocki, M., & Schätzle, A. (2017). Giant data: MapReduce and Hadoop » ADMIN magazine. Retrieved January 10, 2017, from http://www.admin-magazine.com/HPC/Articles/MapReduceand-Hadoop
18. Lee, K.-H., Lee, Y.-J., Choi, H., Chung, Y. D., & Moon, B. (2012). Parallel data processing with MapReduce, ACM SIGMOD Record, 40(4), 11, doi:10.1145/2094114.2094118.
19. Muhammd Jawad Hamid Mughal. Data Mining: Web Data Mining Techniques, Tools and Algorithms: An Overview- (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 6, 2018.

How to Cite This Article:
Santosh Kumar Jha.Technological Prospects of Cloud Computing in Web Mining: Recent Trends and Opportunities. ijetms;7(1):98-104. DOI: 10.46647/ijetms.2023.v07i01.017