A Review on Business Intelligence and Big Data

Authors

DOI:

https://doi.org/10.18201/ijisae.2017533861

Keywords:

Big Data, Business Intelligence, Data Mining, Machine Learning

Abstract

Improvement of data generating, processing, storing and networking technologies has made storing, capturing and sharing of data easier and cheaper than before and has enabled organizations to handle huge volume of data at high velocity and variety, named as big data. Big data offers many opportunities when the associated difficulties are addressed properly. Business Intelligence (BI) basically focuses on transforming raw data into usable, valuable and actionable information for decision-making. It can be classified as a kind of data-driven decision support system. Although big data related papers have increased for last fifteen years, there are not sufficient papers that directly overviews big data impact on BI. As data is growing exponentially, storage, process and analytics tools and technologies become more important for BI solutions. With the advent of big data, BI’s concept, architecture and capabilities are meant to be changed. Unlike a decades before, BI now is to be extract value from huge data ocean by using big data tools as well as classical ones. So, an interclusion has emerged between big data and BI. This paper overviews the current state of the art of BI and big data, and discuss how big data era affects BI solutions in general context.

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Author Biographies

Erkan Sirin, Gazi University

Ph.D. student, Institute of Information Management Information Systems

Hacer Karacan, Gazi University

Assoc.Prof. in Computer Engineering Department

References

Gantz, J. and D. Reinsel, The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the Future, 2012. 2007: p. 1-16.

Gantz, J. and D. Reinsel, Extracting value from chaos. IDC iview, 2011(1142): p. 9-10.

Mayer-Schönberger, V. and K. Cukier, Big data: A revolution that will transform how we live, work, and think. 2013: Houghton Mifflin Harcourt.

Kambatla, K., et al., Trends in big data analytics. Journal of Parallel and Distributed Computing, 2014. 74(7): p. 2561-2573.

Shekhar, H. and M. Sharma, A Framework for Big Data Analytics as a Scalable Systems. 2015.

Beyer, M.A. and D. Laney, The importance of ‘big data’: a definition. Stamford, CT: Gartner, 2012.

Krishnan, K., Data warehousing in the age of big data. 2013: Newnes.

Chen, M., S. Mao, and Y. Liu, Big data: A survey. Mobile Networks and Applications, 2014. 19(2): p. 171-209.

Assunção, M.D., et al., Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 2015. 79: p. 3-15.

Kalapesi, C. Unlocking the value of personal data: From collection to usage. in World Economic Forum technical report. 2013.

Manyika, J., et al., Big data: The next frontier for innovation, competition, and productivity. 2011.

Bertino, E., et al., Challenges and Opportunities with Big Data. 2011.

Chaudhuri, S., U. Dayal, and V. Narasayya, An overview of business intelligence technology. Communications of the ACM, 2011. 54(8): p. 88-98.

Chen, C.P. and C.-Y. Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 2014. 275: p. 314-347.

Labrinidis, A. and H. Jagadish, Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 2012. 5(12): p. 2032-2033.

Brumfiel, G., High-energy physics: Down the petabyte highway. Nature News, 2011. 469(7330): p. 282-283.

Eisenstein, D.J., et al., SDSS-III: Massive spectroscopic surveys of the distant universe, the Milky Way, and extra-solar planetary systems. The Astronomical Journal, 2011. 142(3): p. 72.

Hu, H., et al., Toward scalable systems for big data analytics: A technology tutorial. Access, IEEE, 2014. 2: p. 652-687.

Mayer, M. Innovation at Google: the physics of data. in PARC Forum. 2009.

Maletic, J.I. and A. Marcus. Data Cleansing: Beyond Integrity Analysis. in IQ. 2000. Citeseer.

Poulton, N., Data Storage Networking: Real World Skills for the CompTIA Storage+ Certification and Beyond. 2014: John Wiley & Sons.

Adshead, A., Big Data storage: Defining Big Data and the type of storage it needs. Computer Weekly. ComputerWeekly. com. Published April, 2013.

Brewer, E., CAP twelve years later: How the" rules" have changed. Computer, 2012. 45(2): p. 23-29.

Pokorny, J., NoSQL databases: a step to database scalability in web environment. International Journal of Web Information Systems, 2013. 9(1): p. 69-82.

Ghemawat, S., H. Gobioff, and S.-T. Leung. The Google file system. in ACM SIGOPS operating systems review. 2003. ACM.

McKusick, M.K. and S. Quinlan, GFS: Evolution on Fast-forward. ACM Queue, 2009. 7(7): p. 10.

Tudorica, B.G. and C. Bucur. A comparison between several NoSQL databases with comments and notes. in Roedunet International Conference (RoEduNet), 2011 10th. 2011. IEEE.

Han, J., et al. Survey on NoSQL database. in Pervasive computing and applications (ICPCA), 2011 6th international conference on. 2011. IEEE.

McCreary, D. and A. Kelly, Making sense of NoSQL. Greenwich, Conn.: Manning Publications, 2013.

Vaish, G., Getting started with NoSQL. 2013: Packt Publishing Ltd.

Dean, J. and S. Ghemawat, MapReduce: simplified data processing on large clusters. Communications of the ACM, 2008. 51(1): p. 107-113.

White, C., MapReduce and Data Scientist. 2012, Teradata & Aster.

Begoli, E. and J. Horey. Design principles for effective knowledge discovery from big data. in Software Architecture (WICSA) and European Conference on Software Architecture (ECSA), 2012 Joint Working IEEE/IFIP Conference on. 2012. IEEE.

Fan, W. and A. Bifet, Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 2013. 14(2): p. 1-5.

Wu, X., et al., Data mining with big data. Knowledge and Data Engineering, IEEE Transactions on, 2014. 26(1): p. 97-107.

Russom, P., Big data analytics. TDWI Best Practices Report, Fourth Quarter, 2011.

Khurana, M. and P. Mehta, Big Data Analytics And Technologies. 2015.

Rouse, M., What is big data analytics. Definition from WhatIs. com.[online] Available at: http://searchbusinessanalytics. techtarget. com/definition/big-data-analytics [Accessed: 30 Mar 2014], 2012.

Ahlemeyer-Stubbe, A. and S. Coleman, A practical guide to data mining for business and industry. 2014: John Wiley & Sons.

Alpaydın, E., Introduction to Machine Learning. 2010: Massachusetts Institute of Technology. 579.

Shearer, C., The CRISP-DM model: the new blueprint for data mining. Journal of data warehousing, 2000. 5(4): p. 13-22.

Harrington, P., Machine learning in action. 2012: Manning.

!!! INVALID CITATION !!! {}.

Wu, C., R. Buyya, and K. Ramamohanarao, Big Data Analytics= Machine Learning+ Cloud Computing. arXiv preprint arXiv:1601.03115, 2016.

Rokach, L. and O. Maimon, Supervised Learning, in Data Mining and Knowledge Discovery Handbook. 2009, Springer. p. 133-147.

Provost, F. and T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking. 2013: " O'Reilly Media, Inc.".

Myatt, G.J., Making sense of data: a practical guide to exploratory data analysis and data mining. 2014: John Wiley & Sons.

Olszak, C.M. Dynamic Business Intelligence and Analytical Capabilities in Organizations. in e-Skills for Knowledge Production and Innovation Conference. 2014. Cape Town, South Africa.

Datacomy. THREE FORECASTS YOU SHOULD KNOW: BIG DATA, BUSINESS INTELLIGENCE & ANALYTICS. 2014 02 April 2016].

Duan, L. and L. Da Xu, Business intelligence for enterprise systems: a survey. Industrial Informatics, IEEE Transactions on, 2012. 8(3): p. 679-687.

Cooper, P., Data, information and knowledge. Anaesthesia & Intensive Care Medicine, 2010. 11(12): p. 505-506.

Bellinger, G., D. Castro, and A. Mills, Data, information, knowledge, and wisdom. 2004.

Carter, K.B., Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast! 2014: John Wiley & Sons.

Lim, E.-P., H. Chen, and G. Chen, Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS), 2013. 3(4): p. 17.

Inmon, W.H., Building the data warehouse. 2005: John wiley & sons.

Boateng, O., J. Singh, and G. Singh, Data warehousing. Bus. Intell. J, 2012. 5(2).

Kune, R., et al., The anatomy of big data computing. Software: Practice and Experience, 2016. 46(1): p. 79-105.

Winter, R., O. Marjanovic, and B.H. Wixom, Introduction to the Business Analytics, Business Intelligence and Data Warehousing Minitrack. IEEE Computer Society, 2012: p. 3767.

El-Gayar, O. and P. Timsina. Opportunities for Business Intelligence and Big Data Analytics in Evidence Based Medicine. in System Sciences (HICSS), 2014 47th Hawaii International Conference on. 2014. IEEE.

He, X.J., Business Intelligence and Big Data Analytics: An Overview. Communications of the IIMA, 2016. 14(3): p. 1.

Ong, V.K., Business Intelligence and Big Data Analytics for Higher Education: Cases from UK Higher Education Institutions. Information Engineering Express, 2016. 2(1): p. 65-75.

Bala, M., O. Boussaid, and Z. Alimazighi. P-ETL: Parallel-ETL based on the MapReduce paradigm. in Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on. 2014. IEEE.

Liu, X., C. Thomsen, and T.B. Pedersen, Mapreduce-based dimensional etl made easy. Proceedings of the VLDB Endowment, 2012. 5(12): p. 1882-1885.

Liu, X., C. Thomsen, and T.B. Pedersen, ETLMR: a highly scalable dimensional ETL framework based on mapreduce, in Transactions on Large-Scale Data-and Knowledge-Centered Systems VIII. 2013, Springer. p. 1-31.

Liu, X., C. Thomsen, and T.B. Pedersen. CloudETL: scalable dimensional ETL for hive. in Proceedings of the 18th International Database Engineering & Applications Symposium. 2014. ACM.

Cuzzocrea, A. Analytics over big data: Exploring the convergence of datawarehousing, OLAP and data-intensive cloud infrastructures. in Computer Software and Applications Conference (COMPSAC), 2013 IEEE 37th Annual. 2013. IEEE.

Russom, P., Integrating Hadoop into Business Intelligence and Data Warehousing. TDWI Best Practices Report, 2013.

Wixom, B., et al., The current state of business intelligence in academia: The arrival of big data. Communications of the Association for Information Systems, 2014. 34(1): p. 1.

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Published

12.12.2017

How to Cite

Sirin, E., & Karacan, H. (2017). A Review on Business Intelligence and Big Data. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 206–215. https://doi.org/10.18201/ijisae.2017533861

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Review