Abstract

Now-a-days every organization is moving towards on web based application and cope up lots of data sets. Data may be structured, unstructured or semi-structure. These data need processing, analysis and storage in proper format using innovative techniques and methodologies. Big data parameterized into three basic categories Volume, Variety and Velocity. The social media like Facebook, Whatsapp, Twitter, hike. are generating lots of data in a day in the form of text-messages, audio-recording, images, videos etc. The problem which appears is how to manage this huge data in a systematic way because users want quick response on web search or on smart phone access using web apps. In this paper we have studied how big data invoke in different areas like social media, atmosphere, hospitals, research centers etc. We have suggested contributions in big data categories, applications in different area’s like in Machine learning, Social Network, Bio-Informatics, Data Mining, and Clouds along with challenges, future scope and storage prospects.

References

• Marr, Bernard, 2015. “A brief history of big data everyone should read”, available at: https:// www .linkedin.com/pulse/brief-history-big-data-everyone-should-read-bernard-marr.
• Jagdish,H.V. , “Big Data and Science:Myths and Reality”, Journal of Big Data Research, 2015, Elsevier, pp. 49-52.
• Cárdenas, Alvaro A., 2013. “Big Data analytics for security intelligence” Cloud Security Alliance, available at: www.cloudsecurity alliance.org/ research/big-data.
• Singh, Dilpreet and Reddy, Chandan K. , “A survey on platforms for big data analytics”, Journal of Big Data, 2014, Springer Open, 2:8, pp. 1-20.
• Ding Junhua, Hu Xin-Hua, and Gudivada Venkat , “A Machine Learning Based Framework for Verification and Validation of Massive Scale Image Data”, IEEE Transaction on Big Data , 2017,  Issue: 99, pp. 1-18.
• Olshannikova Ekaterina , Olsson Thomas, Huhtamäki Jukka and Hannu Kärkkäinen, “Conceptualizing Big Social Data”, Journal of Big Data, 2017, Springer Open, 4:3,pp. 1-19.
• Barrachina Arantxa Duque and O’Driscoll Aisling , “A big data methodology for categorising technical support requests using Hadoop and Mahout”, Journal of Big Data, 2014, Springer Open.1:1, pp. 1-11.
• TargioHashem,IbrahimAbaker , Yaqoob,Ibrar , Anuar ,Nor Badrul , Mokhtar ,Salimah, Gani ,Abdullah,and Khan ,SameeUllah , “the rise of big data on cloud computing: Review and open research issues”, journal of Information System, 2015, Elsevier, vol 47, pp. 98-115.
• Inukollu, Venkata Narasimha, Arsi ,Sailaja and Rao Ravuri ,Srinivasa , “Security Issues Associated With Big Data In Cloud Computing”, International Journal of Network Security & Its Applications (IJNSA), 2014, Vol.6, No.3, pp. 45-56.
• Varma,P.China Venkanna , “Analysis of a Network IO Botleneck in Big Data Environments Based on Docker Containers”,Journal of Big Data Research, 2016, Elsevier, Vol 3, pp. 24-28.
• Zheng, Charles, and Thain, Douglas 2015. Integrating Containers into Workflows: A Case Study Using Makeflow, Work Queue, and Docker. In Proceeding of the 8th International Workshop on Virtualization Technologies in Distributed Computing, pp. 31-38.
• Sawant,Poonam G.,and Desai, B.L., “Big Data Mining: Challenges and Opportunities to Forecast Future Scenario”, International Journal of Innovative Research in Computer and Communication Engineering, 2015, Vol. 3, Issue 6, pp. 5228-5232
• Harsoor,Anita S.,and Patil,Anushree , “Forecast Of Sales Of Walmart Store Using Big Data Applications”, International Journal of Research in Engineering and Technology, 2015, Volume 04, Issue 06, pp. 51-59
• Fan,Wei, and Bifet,Albert 2013. “Mining BigData:Current Status, and Forecast to the Future”, Volume 14, Issue 2, pp. 1-5.
• Liu, Na, and Zhou, Jianfei , “The Research and Application of a Big Data Storage Model”, International Journal of Database Theory and Application, 2015, Vol.8, No.4 , pp. 319-330.
• Li, yang, Guo, Li and Guo, Yike 2012, An Efficient and Performance- Aware Big Data Storage System. In Proceeding of the CLOSER 2012 International Conference on Cloud Computing and Services science, pp. 102-116.
• Li, Bo 2013. Survey of Recent Research Progress and Issues in Big Data, available at: http :// www.cs.wustl.edu/~jain/cse570-13/ftp/bigdata2/
• Simo, Hervais 2015. Big Data: Opportunities and Privacy Challenges, available at: https://arxiv.org /ftp/arxiv/papers/1502/1502.00823.pdf
• Zeng,Gang , “Research on Privacy Protection in Big Data Environment”, Journal of Engineering Research and Applications, 2015, Vol. 5, Issue 5, ISSN : 2248-9622.
• Report To The President Big Data and Privacy: A Technological Perspective 2014. available at: https://www.whitehouse.gov/blog/2014/05/01/pcast-releases-report-big-data-and-privacy
• European Commission- Directorate-General for Health and Consumers Unit D3 eHealth and Health Technology Assessment 2014. The Use of Big Data in Public Health Policy and Research, available at: http://ec.europa.eu/health/ehealth/docs/ev_201411 18_co07b_en.pdf
• Coulton,Claudia J. , Goerge ,Robert, Putnam-Hornstein ,Emily and Haan ,Benjamin de 2015. Harnessing Big Data for Social Good: A Grand Challenge for Social Work ,American Academy of Social Work and Social Welfare, available at: http://aaswsw.org/wp-content/uploads/2015/12 / W P11-with-cover.pdf
• Tan,Wei, Blake ,M. Brian , Saleh, Iman and Dustdar ,Schahram , “ Social-Network-Sourced Big Data Analytics”, IEEE Internet Computing, IEEE Computer Society, 2013, volume 17, issue 5, 62-69.
• Moniruzzaman,A. B. M. and Hossain,Syed Akhter , “ NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison”, International Journal of Database, Theory and Application, 2013, Vol. 6, No. 4, pp. 1-13.
• Tsai, Chun, Wei, Lai ,Chin‑Feng, Chao ,Han‑Chieh and Vasilakos, Athanasios V. , “ Big data analytics: a survey”, Journal of Big Data, Springer Open, 2015, volume 2, issue 21, pp. 1-32.
• Zhou, Zhi-Hua , “Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives”, IEEE Computational Intelligence Magazine, 2014, available at: http://ieeexplore .ieee .org/document/6920114/
• Najafabadi,Maryam M, Villanustre ,Flavio, Khoshgoftaar ,Taghi M, Seliya ,Naeem, Wald ,Randall and Muharemagic, Edin , “ Deep learning applications and challenges in big data analytics”, Journal of Big Data, 2015, Springer Open Journal, 2:1, pp. 1-21.
• Tarwani,Kanchan M., Saudagar ,Saleha S. and Misalkar ,Harshal D. , “Machine Learning in Big Data Analytics: An Overview”,International Journal of Advanced Research in Computer Science and Software Engineering, 2015, Volume 5, Issue 4, pp. 270-274.
• Kashyap,Hirak, Ahmed ,Hasin Afzal, Hoque, Nazrul, Roy,Swarup and Kumar Bhattacharyya, Dhruba 2015. Big Data Analytics in Bioinformatics: A Machine Learning Perspective, available at: http://arxiv.org/pdf/1506.05101.pdf
• Chen, Min, and Mao, Shiwen, Liu, Yunhao , “Big Data: A survey”, Mobile Network and Applications, Springer Link, 2014, volume 19, issue 2, pp. 171-209.
• Kaisler, Stephen, 2013. Big Data: Issues and Challenges Moving Forward”, 46th Hawaii International Conference on System Sciences, IEEE, dio 10.1109/HICSS.2013.645
• Special Issue on Big Data, Analytics and High Performance Computing, available at: http:/ / www .journals.elsevier.com/big-data-research/call-for-papers/special-issue-on-big-data-analytics-and-high-performance-com
• Lee, Jae-Gil and Kang Minseo , “Geospatial Big Data: Challenges and Opportunities”, Journal of Big Data Research, 2015, Elsevier, pp. 74-81.
• Mukherjee, Samiddha, and Shaw, Ravi , “Big Data – Concepts, Applications, Challenges and Future Scope”, International Journal of Advanced Research in Computer and Communication Engineering, 2016, volume 5, issue 2, pp. 66-74.
• Fan, Shaokun, Lau Raymond Y.K. and Zhao J. Leon, “Demystifying big data analytics for business intelligence through the lens of marketing mix”, Journal of Big Data Research, 2015, Elsevier, volume 2, issue 1, pp. 28-32.