ANWESH: International Journal of Management & Information Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Data Mining from the Big Data


Affiliations
1 Galgotia University, Greater Noida, Uttar Pradesh, India
     

   Subscribe/Renew Journal


We are already in the era of Exa Bytes where data comes with different V's (Volume, Velocity, Variety etc. [12]. Data sources are decentralized and diverse. Data is dynamic and having complex relationships. Existing data mining tools and technologies are not very effective and have some limitations too. Earlier data stored in Warehouses have some schema standard model which leads to effective and efficient information mining. Now, information industry needs to develop a efficient and reliable NoSQL technology that is able to handle the unstructured and dynamic data. This paper summarizes the methodologies that are already developed, and the key challenges and the privacy issues.

Keywords

Mining, Unstructured, IoT, Data Mining, Complex, Heterogeneity.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Mashey, J. R. (1998). Big Data and the Next Wave of Infra Stress.

  • Gil Press. (2013). A Very Short History of Big Data. Retrieved from http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-bigdata/

  • Anderson, P. (2007). What is Web 2.0? Ideas, technologies and implications for education. JISC Technology and Standards Watch, Feb.

  • Aghaei, S., Nematbakhsh, M. A., & Farsani, H. K. (2012). Evolution of the world wide web: From Web 1.0 TO WEB 4.0. International Journal of Web & Semantic Technology (IJWesT) 3(1).

  • Anonymous Internet Live Stats (elaboration of databy International Telecommunication Union (ITU) and United Nations Population Division). Retrieved from http://www.internetlivestats.com/internet-users/#definitions.

  • “IBM What Is Big Data: Bring Big Data to the Enterprise. Retrieved from http:// www-01.ibm.com/software/data/bigdata/, IBM, 2012.

  • Acharya, S., & Chellappan, S. (2015). Willey big data and analytics. ISBN : 978-81-265-5478-2.

  • Simlilearn. What is Big Data /Hadoop Tutorial/Big Data and Hadoop Training. (2015) Retrieved from https://www.youtube.com/watch?v=CKLzDWMsQGM

  • Lomotey, R. K., & Deters, R. (2013). RSenter: Tool for Topics and Terms Extraction from Unstructured Data Debris, Proc. of the 2013 IEEE International Congress on Big Data (BigData Congress 2013), (pp:395-402), Santa Clara, California.

  • Lomotey, R. K., & Deters, R. (2014). Towards Knowledge Discovery in Big Data, 2014 IEEE 8th International Symposium on Service Oriented System Engineering.

  • Wu, X. (2014). Fellow, IEEE, Xingquan Zhu, Senior Member, IEEE, Gong-Qing Wu, and Wei Ding, Senior Member, IEEE, Data Mining with Big Data, IEEE Transactions on Knowledge and Data Engineering, 26(1).

  • Laney, D. (2001) 3-d data management: controlling data volume, velocity and variety. META Group Research Note, 6 February.

  • Srivastava, S., & Srivastava, R. (2012). Significance of reverse logistics system to control e-waste, 2(1).

  • Srivastava, S., & Srivastava, R. (2012). Adoption of Green Information Technology (GIT) In India-A Current Scenerio. Journal of Information and Operations Management, 3.1(61).


Abstract Views: 245

PDF Views: 0




  • Data Mining from the Big Data

Abstract Views: 245  |  PDF Views: 0

Authors

Satyajee Srivastava
Galgotia University, Greater Noida, Uttar Pradesh, India

Abstract


We are already in the era of Exa Bytes where data comes with different V's (Volume, Velocity, Variety etc. [12]. Data sources are decentralized and diverse. Data is dynamic and having complex relationships. Existing data mining tools and technologies are not very effective and have some limitations too. Earlier data stored in Warehouses have some schema standard model which leads to effective and efficient information mining. Now, information industry needs to develop a efficient and reliable NoSQL technology that is able to handle the unstructured and dynamic data. This paper summarizes the methodologies that are already developed, and the key challenges and the privacy issues.

Keywords


Mining, Unstructured, IoT, Data Mining, Complex, Heterogeneity.

References