Abstract
Nowadays, Big Data is considered to be the emerging field of research. However, it has gained momentum a decade ago and is still in its infancy stage. Big Data is a huge volume of data that is produced from various sources and thus traditional database systems are incapable of processing such a voluminous data. This is the need of hour to use advanced tools and methods to get value from Big Data. However, the pace of data generation is greater than ever which leads to numerous challenges such as data inconsistency, security, timeliness and scalability. Here, in this paper, a brief introduction to the Big Data technology and its importance in various fields. This paper mainly focuses on characteristics of Big Data and gives insights of a brief clarification of challenges related to Big Data and some analytical methods such as Hadoop and MapReduce. The various tools and techniques that can be used in Big Data technology have been discussed further.
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References
Jaseena, K.U., David, J.M.: Issues, challenges, and solutions: big data mining. Comput. Sci. Inf. Technol. (CS&IT) 131–140 (2014)
Lawal, Z.K., Zakari, R.Y., Shuaibu, M.Z., Bala, A.: A review: issues and challenges in big data from analytic and storage perspectives. Int. J. Eng. Comput. Sci. 5(3), 15947–15961 (2016)
Holzinger, A., Stocker, C., Ofner, B., Prohaska, G., Brabenetz, A., Hofmann-Wellenhof, R.: Combining HCI, natural language processing, and knowledge discovery-potential of IBM content analytics as an assistive technology in the biomedical field. In: Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 13–24). Springer, Berlin (2013)
Landset, S., Khoshgoftaar, T.M., Richter, A.N., Hasanin, T.: A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J. Big Data 2(1), 24 (2015)
Jin, X., Wah, B.W., Cheng, X., Wang, Y.: Significance and challenges of big data research. Big Data Res. 2(2), 59–64 (2015)
Kaur, H., Alam, M.A., Jameel, R., Mourya, A., Chang, V.A.: Proposed solution and future direction for blockchain-based heterogeneous medicare data in cloud environment. J. Med. Sys. 42(8) (2018)
Anuradha, J.: A brief introduction on Big Data 5Vs characteristics and Hadoop technology. Procedia Comput. Sci. 48, 319–324 (2015)
Kaur, H., Lechman, E., Marszk, A.: Catalyzing Development through ICT Adoption: The Developing World Experience, pp. 288. Springer Publishers, Switzerland  (2017)
Beyer, M.A., Laney, D.: The Importance of ‘Big Data’: A Definition, pp. 2014–2018. Gartner, Stamford (2012)
Kaisler, S., Armour, F., Espinosa, J.A., Money, W.: Big data: issues and challenges moving forward. In: 2013 46th Hawaii International Conference on System Sciences (HICSS) (pp. 995–1004). IEEE (2013)
Mervis, J. (2012). Agencies rally to tackle big data
Hey, T., Tansley, S., Tolle, K.M.: The Fourth Paradigm: Data-Intensive Scientific Discovery, vol. 1. Microsoft Research, Redmond (2009)
O’Neil, C., Schutt, R.: Doing Data Science: Straight Talk from the Frontline. O’Reilly Media, Inc. (2013)
Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 1165–1188 (2012)
Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012 (2009)
Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iview 1142(2011), 1–12 (2011)
Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) (pp. 1–10). Ieee (2010)
Ghemawat, S., Gobioff, H., Leung, S.T.: The Google File System, 37(5), 29–43. ACM (2003)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Ahrens, J., Hendrickson, B., Long, G., Miller, S., Ross, R., Williams, D.: Data-intensive science in the US DOE: case studies and future challenges. Comput. Sci. Eng. 13(6), 14–24 (2011)
Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V.: Critical analysis of Big Data challenges and analytical methods. J. Bus. Res. 70, 263–286 (2017)
Lu, R., Zhu, H., Liu, X., Liu, J.K., Shao, J.: Toward efficient and privacy-preserving computing in big data era. IEEE Netw. 28(4), 46–50 (2014)
Khan, N., Yaqoob, I., Hashem, I.A.T., Inayat, Z., Ali, M., Kamaleldin, W., Alam, M., Shiraz, M., Gani, A.: Big data: survey, technologies, opportunities, and challenges. Sci. World J. (2014)
Zikopoulos, P., Eaton, C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media (2011)
Demchenko, Y., Grosso, P., De Laat, C., Membrey, P.: Addressing big data issues in scientific data infrastructure. In: 2013 International Conference on Collaboration Technologies and Systems (CTS) (pp. 48–55). IEEE (2013)
Yi, X., Liu, F., Liu, J., Jin, H.: Building a network highway for big data: architecture and challenges. IEEE Netw. 28(4), 5–13 (2014)
Samuel, S.J., RVP, K., Sashidhar, K., Bharathi, C.R.: A survey on big data and its research challenges. ARPN J. Eng. Appl. Sci. 10(8), 3343–3347 (2015)
Bezdek, J.C.: Objective function clustering. In: Pattern Recognition with Fuzzy Objective Function Algorithms (pp. 43–93). Springer, Boston (1981)
Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning-a new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)
Mitra, P., Murthy, C.A., Pal, S.K.: A probabilistic active support vector learning algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 26(3), 413–418 (2004)
Mansour, Y., Chang, A.Y., Tamby, J., Vaahedi, E., Corns, B.R., El-Sharkawi, M.A.: Large scale dynamic security screening and ranking using neural networks. IEEE Trans. Power Syst. 12(2), 954–960 (1997)
Fujimoto, Y., Fukuda, N., Akabane, T.: Massively parallel architectures for large scale neural network simulations. IEEE Trans. Neural Netw. 3(6), 876–888 (1992)
Ahn, J.B.: Neuron machine: parallel and pipelined digital neurocomputing architecture. In: 2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom) (pp. 143–147). IEEE (2012)
Ma, H., King, I., Lyu, M.R.: Mining web graphs for recommendations. IEEE Trans. Knowl. Data Eng. 24(6), 1051–1064 (2012)
White, T.: Hadoop: the definitive guide: the definitive guide. O’Reilly Media, Inc. (2009)
Ingersoll, G.: Introducing Apache Mahout. Scalable, Commercial Friendly Machine Learning for Building Intelligent Applications. IBM (2009)
Rong, C.: November. Using Mahout for clustering Wikipedia’s latest articles: a comparison between k-means and fuzzy c-means in the cloud. In 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom), pp. 565–569. IEEE (2011)
Chauhan, R., Kaur, H., Lechman, E., Marszk, A.: Big data analytics for ICT monitoring and development. In: Kaur et al. (eds.) Catalyzing Development through ICT Adoption: The Developing World Experience, pp. 25–36. Springer (2017)
Storm. http://storm-project.net (2012)
Chauhan, J., Chowdhury, S.A., Makaroff, D.: Performance evaluation of Yahoo! S4: a first look. In: 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 58–65). IEEE (2012)
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Ul Ahsaan, S., Kaur, H., Naaz, S. (2020). An Empirical Study of Big Data: Opportunities, Challenges and Technologies. In: Patnaik, S., Ip, A., Tavana, M., Jain, V. (eds) New Paradigm in Decision Science and Management. Advances in Intelligent Systems and Computing, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-9330-3_6
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DOI: https://doi.org/10.1007/978-981-13-9330-3_6
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