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Erschienen in:
Need for Developing Intelligent Interfaces for Big Data Analytics in the Microfinance Industry Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Big Data: Issues, Challenges, and Techniques in Business Intelligence

verfasst von : Mudasir Ahmad Wani, Suraiya Jabin

Erschienen in: Big Data Analytics

Verlag: Springer Singapore

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Abstract

During the last decade, the most challenging problem the world envisaged was big data problem. The big data problem means that data is growing at a much faster rate than computational speeds. And it is the result of the fact that storage cost is getting cheaper day by day, so people as well as almost all business or scientific organizations are storing more and more data. Social activities, scientific experiments, biological explorations along with the sensor devices are great big data contributors. Big data is beneficial to the society and business but at the same time, it brings challenges to the scientific communities. The existing traditional tools, machine learning algorithms, and techniques are not capable of handling, managing, and analyzing big data, although various scalable machine learning algorithms, techniques, and tools (e.g., Hadoop and Apache Spark open source platforms) are prevalent. In this paper, we have identified the most pertinent issues and challenges related to big data and point out a comprehensive comparison of various techniques for handling big data problem.

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Vorheriges Kapitel Fully Homomorphic Encryption Scheme with Probabilistic Encryption Based on Euler’s Theorem and Application in Cloud Computing
Nächstes Kapitel Cloud Computing in Bioinformatics and Big Data Analytics: Current Status and Future Research
Literatur
1.
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)
2.
Katal, A., Wazid, M., Goudar, R.: Big data: issues, challenges, tools and good practices. In: 2013 Sixth International Conference on Contemporary Computing (IC3), pp. 404–409. IEEE (2013)
3.
Fan, J., Han, F., Liu, H.: Challenges of big data analysis. Natl. Sci. Rev. 1(2), 293–314 (2014)CrossRef
4.
Beyer, M.A., Laney, D.: The Importance of Big Data: A Definition. Gartner, Stamford (2012)
5.
Laney, D.: 3d data management: controlling data volume, velocity and variety. META Group Research Note, 6, 70 (2001)
6.
Minelli, M., Chambers, M., Dhiraj, A.: Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. Wiley, New York (2012)
7.
Vossen, G.: Big data as the new enabler in business and other intelligence. Vietnam J. Comput. Sci. 1(1), 3–14 (2014)CrossRef
8.
Laney, D.: 3D data management: controlling data volume, velocity and variety. META Group Research Note, 6, 70 (2001)
9.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaumann Publishers, United States of America (2001)MATH
10.
Jabin, S., Zareen, F.J.: Biometric signature verification. Int. J. Biom. 7(2), 97–118 (2015)CrossRef
11.
Jabin, S.: Stock market prediction using feed-forward artificial neural network. Int. J. Comput. Appl. 99(9), 4–8 (2014)
12.
Jabin, S.: Learning classifier systems approach for automated discovery of hierarchical censored production rules. In: Information and Communication Technologies, pp. 68-77. Springer, Berlin (2010)
13.
Xiong, H.Y., Alipanahi, B., Lee, L.J., Bretschneider, H., Merico, D., Yuen, R.K., Hua, Y., Gueroussov, S., Najafabadi, H.S., Hughes, T.R., et al.: The human splicing code reveals new insights into the genetic determinants of disease. Science 347(6218), 1254806 (2015)CrossRef
14.
Kellis, M., Wold, B., Snyder, M.P., Bernstein, B.E., Kundaje, A., Marinov, G.K., Ward, L.D., Birney, E., Crawford, G.E., Dekker, J., et al.: Defining functional DNA elements in the human genome. Proc. Natl. Acad. Sci. 111(17), 6131–6138 (2014)CrossRef
15.
Tsikerdekis, M., Zeadally, S.: Multiple account identity deception detection in social media using nonverbal behavior. IEEE Trans. Inf. Forensics Secur. 9(8), 1311–1321 (2014)CrossRef
16.
Schön, D.A., Argyris, C.: Organizational learning: a theory of action perspective. Reis: Revista española de investigaciones sociológicas 77, 345–350 (1997)
17.
Ebner, K., Buhnen, T., Urbach, N.: Think big with big data: identifying suitable big data strategies in corporate environments. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 3748–3757. IEEE (2014)
18.
19.
Che, D., Safran, M., Peng, Z.: From big data to big data mining: challenges, issues, and opportunities. In: Database Systems for Advanced Applications, pp. 1–15. Springer, Berlin (2013)
20.
Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)
21.
22.
Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)CrossRef
23.
Buhl, H.U., Röglinger, M., Moser, D.K.F., Heidemann, J.: Big data. Bus. Inf. Syst. Eng. 5(2), 65–69 (2013)CrossRef
24.
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef
25.
Wu, K.: Fastbit: an efficient indexing technology for accelerating data-intensive science. In: Journal of Physics: Conference Series, vol. 16, p. 556. IOP Publishing (2005)
26.
Dittrich, J., Quiane-Ruiz, J.A.: Efficient big data processing in hadoop MapReduce. Proc. VLDB Endowment 5(12), 2014–2015 (2012)CrossRef
27.
Triguero, I., Peralta, D., Bacardit, J., Garc a, S., Herrera, F.: MRPR: a MapReduce solution for prototype reduction in big data classification. Neurocomputing 150, 331–345 (2015)
28.
Neumeyer, L., Robbins, B., Nair, A., Kesari, A.: S4: distributed stream computing platform. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 170–177 (2010)
29.
Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. In: Information Conference on Cloud System and Big Data Engineering, pp. 404–409. IEEE (2013)
30.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: the next frontier for innovation, competition, and productivity (2011)
31.
Kim, G.H., Trimi, S., Chung, J.H.: Big-data applications in the government sector. Commun. ACM 57(3), 78–85 (2014)CrossRef
32.
Stonebraker, M., Hong, J.: Researchers ‘big data crisis; understanding design and functionality. Commun ACM 55(2), 10–11 (2012)CrossRef
33.
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, vol. 10, p. 10 (2010)
34.
Driscoll, A.O., Daugelaite, J., Sleator, R.D.: Big data, Hadoop and cloud computing in genomics. J. Biomed. Inf. 46(5), 774–781 (2013)
35.
Simoff, S., Bohlen, M.H., Mazeika, A.: Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, vol. 4404. Springer Science & Business Media (2008)
36.
Sawant, N., Shah, H.: Big data visualization patterns. In: Big Data Application Architecture Q&A, pp. 79–90 (2013)
37.
38.
39.
40.
Gu, L., Li, H.: Memory or time: performance evaluation for iterative operation on hadoop and spark. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC EUC), pp. 721–727. IEEE (2013)
41.
Zheng, X., Zeng, Z., Chen, Z., Yu, Y., Rong, C.: Detecting spammers on social networks. Neurocomputing 159, 27–34 (2015)CrossRef
42.
Conti, M., Poovendran, R., Secchiero, M.: Fakebook: detecting fake profiles in on-line social networks. In: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 1071–1078. IEEE Computer Society (2012)
43.
Anwar, T., Abulaish, M.: Ranking radically influential web forum users. IEEE Trans. Inf. Forensics Secur. 10(6), 1289–1298 (2015)CrossRef
44.
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al.: The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20(9), 1297–1303 (2010)CrossRef
45.
46.
47.
Marx, V.: Biology: the big challenges of big data. Nature 498(7453), 255–260 (2013)CrossRef
48.
Assuncao, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A., Buyya, R.: Big data computing and clouds: trends and future directions. J. Parallel Distrib. Comput. 79, 3–15 (2015)CrossRef
49.
Kambatla, K., Kollias, G., Kumar, V., Grama, A.: Trends in big data analytics. J. Parallel Distrib. Comput. 74(7), 2561–2573 (2014)CrossRef
50.
Shanahan, J.G., Dai, L.: Large scale distributed data science using apache spark. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2323–2324. ACM (2015)
51.
Stich, V., Jordan, F., Birkmeier, M., Oazgil, K., Reschke, J., Diews, A.: Big data technology for resilient failure management in production systems. In: Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth, pp. 447–454. Springer, Berlin (2015)
52.
Agrawal, D., Das, S., El Abbadi, A.: Big data and cloud computing: current state and future opportunities. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 530–533. ACM (2011)
53.
McDaniel, M.A.: Big-brained people are smarter: a meta-analysis of the relation-ship between in vivo brain volume and intelligence. Intelligence 33(4), 337–346 (2005)CrossRef
54.
Tan, K.H., Zhan, Y., Ji, G., Ye, F., Chang, C.: Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. Int. J. Prod. Econ. 165, 223–233 (2015)CrossRef
55.
Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mobile Netw. Appl. 19(2), 171–209 (2014)CrossRef
Metadaten
Titel
Big Data: Issues, Challenges, and Techniques in Business Intelligence
verfasst von
Mudasir Ahmad Wani
Suraiya Jabin
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Big Data Analytics

Print ISBN: 978-981-10-6619-1

Electronic ISBN: 978-981-10-6620-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-6620-7_59

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