Authors:
G. Ranadheer Reddy,V.Pranathi,P. Pramod Kumar,DOI NO:
https://doi.org/10.26782/jmcms.2020.08.00039Keywords:
Web mining,levels of data mining,Abstract
A ubiquitous process to evoke the most needed data information from huge amount of unprocessed data to analyze the patterns is called as data mining which is also named as data through knowledge discovery. It helps the enterprises to extract the data information to gain knowledge for better. [I] Data mining usually deals with text for mining. Since we are using internet for the accessibility of data. In other words, we are making use of web to extract the data, modify and process the text using the WebPages. Evoking the information data which is present on internet is done using data mining is called as Web Mining.[II] It is an integral part of data mining for searching and analyzing the pattern. There are various data resources to obtain the data from web which is categorized into metadata, text documents, web links and web content. A web mining also consist of images, videos and audio information data which are considered as multimedia data. As, many users are more keen towards extracting information in form of images and videos from the web pages , so there’s a need of bringing out the required multimedia data information from unused scattered multimedia data present in the web. Here, we need to coalesce mining concepts through web into the multimedia stored data. Such concept is considered as Multimedia Web Mining, [V]It reaps the hidden information of a multimedia file as metadata, represents relationship between multimedia data files]5. For better and efficient working performance of mining techniques, multimedia mining also index and classify the various modes of multidata such as animation, moving, still , playback and video modes. Multimedia information is divided into two halves as organized and semi organized. Similarly web mining is categorized into utilization mining, organized mining and substance mining. In this paper, we explore the integration of multimedia with web mining for better enhancement in achieving the classification of data.Refference:
I. http://airccse.org/journal/ijcga/papers/5115ijcga05.pdf
II. https://www.researchgate.net/publication/319404075_A_Survey_on_Web_Mining_Techniques_and_Applications
III. https://www.researchgate.net/publication/230639907_A_Survey_on_Multimedia_Data_Mining_and_Its_Relevance_Today
IV. http://www.ijcstjournal.org/volume-5/issue-3/IJCST-V5I3P21.pdf
V. http://www.academia.edu/Documents/in/Web_Data_Mining
VI. https://ieeexplore.ieee.org/abstract/document/5992597
VII. https://arxiv.org/pdf/1109.1145
VIII. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
IX. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
X. Kiran Kumar S V N Madupu, “Data Mining Model for Visualization as a Process of Knowledge Discovery”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 – 8875, Vol. 1, Issue 4, October 2012.
XI. Kiran Kumar S V N Madupu, “Advanced Database Systems and Technology Progress of Data Mining”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319 – 8753, Vol. 2, Issue 3, March 2013
XII. Kiran Kumar S V N Madupu, “Functionalities, Applications, Issues and Types of Data Mining System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 8, August 2017
XIII. M. M. Alabbadi, “Cloud Computing for Education and Learning: Education and Learning as a Service (ELaaS),” 2011 14th InternationalConferenceonInteractiveCollaborativeLearning(ICL), pp. 589 – 594, DOI=21-23 Sept.2011.
XIV. Naresh Kumar, S., Pramod Kumar, P., Sandeep, C.H. & Shwetha, S. 2018, “Opportunities for applying deep learning networks to tumour classification”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 742-747.
XV. Pramod Kumar, P., Sandeep, C.H. & Naresh Kumar, S. 2018, “An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 722-725.
XVI. Pramod Kumar P,Thirupathi V, Monica D, “Enhancements in Mobility Management for Future Wireless Networks”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2,Issue 2, February 2013
XVII. Pramod Kumar P, CH Sandeep, Naresh Kumar S, “An Overview of the Factors Affecting Handovers and EffectiveHighlights of Handover Techniques for Next GenerationWireless Networks”, Indian Journal of Public Health Research &Development, November 2018, Vol.9, No. 11
XVIII. P. Pramod Kumar, K. Sagar, “Vertical Handover Decision Algorithm Based On Several Specifications in Heterogeneous Wireless Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-9, July 2019
XIX. P. Pramod Kumar ,Dr. K. Sagar, “A proficient and smart electricity billing management system ” ,International Conference on Emerging Trends in Engineering and published in Springer Nature as a Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3), July 2019.
XX. PushpavathiMannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XXI. Pushpa Mannava, “Research Challenges and Technology Progress of Data Mining with Bigdata”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume5 Issue 4, pp. 08-315, July-August 2019. Available at doi : https://doi.org/10.32628/CSEIT20627
XXII. Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN: 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XXIII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XXIV. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XXV. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XXVI. P. Kalagiakos “Cloud Computing Learning,” 2011 5th International Conference on Application of Information and Communication Technologies (AICT), Baku pp. 1 – 4, DOI=12-14 Oct.2011.
XXVII. Ramesh Gadde, Namavaram Vijay, “A SURVEY ON EVOLUTION OF BIG DATA WITH HADOOP” in “International Journal of Research In Science & Engineering”, Volume: 3 Issue: 6 Nov-Dec 2017.
XXVIII. Sandeep, C.H., Naresh Kumar, S. & Pramod Kumar, P. 2018, “Security challenges and issues of the IoT system”, Indian Journal of Public Health Research and Development, vol. 9, no. 11, pp. 748-753.
XXIX. Seena Naik, K. & Sudarshan, E. 2019, “Smart healthcare monitoring system using raspberry Pi on IoT platform”, ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 4, pp. 872-876.
XXX. Sheshikala, M., Kothandaraman, D., Vijaya Prakash, R. & Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.
XXXI. Shailaja, P., Guru Rao, C.V. &Nagaraju, A. 2019, “A parametric oriented research on routing algorithms in mobile adhoc networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 4116-4126.
XXXII. Sivakumar, M., Ramakrishna, M.S., Subrahmanyam, K.B.V. &Prabhandini, V. 2017, “Model Order Reduction of Higher Order Continuous Time Systems Using Intelligent Search Evolution Algorithm”, Proceedings – 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, ICRTEECT 2017, pp. 70.
XXXIII. Shailaja, G.K. & Rao, C.V.G. 2019, “Robust and lossless data privacy preservation: optimal key based data sanitization”, Evolutionary Intelligence.
XXXIV. Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XXXV. Sriramoju Ajay Babu, Namavaram Vijay and Ramesh Gadde, “An Overview of Big Data Challenges, Tools and Techniques”in “International Journal of Research and Applications”, Oct – Dec, 2017 Transactions 4(16): 596-601
XXXVI. Srinivas, Chintakindi& Rao, Chakunta& Radhakrishna, Vangipuram. (2018). Feature Vector Based Component Clustering for Software Reuse. 1-6. 10.1145/3234698.3234737.
XXXVII. Subba Rao, A. &Ganguly, P. 2018, “Implementation of Efficient Cache Architecture for Performance Improvement in Communication based Systems”, International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, pp. 1192.
XXXVIII. Venkatramulu, S. & Rao, Chakunta. (2018). CSES: Cuckoo Search Based Exploratory Scale to Defend Input-Type Validation Vulnerabilities of HTTP Requests. 10.1007/978-981-10-8228-3_23.Venkatramulu, S. & Guru Rao, C.V. 2017, “RPAD: Rule based pattern discovery for input type validation vulnerabilities detection & prevention of HTTP requests”, International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 14033-14039
XXXIX. W. Dawoud, I. Takouna, and C. Meinel, “Infrastructure as a Service Security: Challenges and Solutions,” 2010 7thInternational Conference on Informatics and System, pp. 1-8, March2010.
XL. W. Itani, A. Kayssi, and A. Chehab, “Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures,” 2009 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009, pp.711-716.