Big Data Analytics With Business Intelligence: A Survey

Authors

  • Revati. K.  Student, Department of ISE, VVCE, Mysuru, Karnataka, India
  • Padmashree. V.  Student, Department of ISE, VVCE, Mysuru, Karnataka, India
  • Veeresh Hiremath  Student, Department of ISE, VVCE, Mysuru, Karnataka, India
  • Vinutha. D. C.  Associate professor, Department of ISE, VVCE, Mysuru, Karnataka, India
  • Chandini. S. B.  Associate professor, Department of ISE, VVCE, Mysuru, Karnataka, India

Keywords:

Abstract

Everything in today’s world stands on data. Usage of many applications is resulting in the generation of several petabytes of data every day. This generated data is very important in order to take business decisions. Thus to analyse this big data business intelligence systems are built. There are many platforms to perform this analysis such as hadoop, spark, orange etc. and also there are many algorithms to perform scheduling such as FCFS, capacity scheduling, priority scheduling, shortest job scheduling etc. The main aim of this paper is to build an efficient business intelligence system which uses a scheduling algorithm called MSHEFT-“Memory sensitive heterogeneous earliest finish time”.

References

  1. Bao Rong Chang, Yo-Ai Wang, Yun-Da Lee, and Chien-Feng Huang “Development of Multiple Big Data Analysis Platforms for Business Intelligence”. Proceedings of the 2017 IEEE International Conference on Applied System Innovation IEEE-ICASI 2017 - Meen, Prior & Lam (Eds).
  2. Nagina, Dr. Sunita Dhingra – “Scheduling Algorithms in Big Data: A Survey “. International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 5 Issue 8 August 2016 Page No. 17737-17743 .
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  4. ”Business intelligence and analytics:from big data to big impact” Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {[email protected]} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {[email protected]} Veda C. Storey J. Mack Robinson College of Business, Georgia State University.
  5. Optimizing Load Balancing and Data-Locality with Data-aware Scheduling Ke Wang*, Xiaobing Zhou§, Tonglin Li*, Dongfang Zhao*, Michael Lang†, Ioan Raicu*‡ *Illinois Institute of Technology, §Hortonworks Inc., †Los Alamos National Laboratory, ‡Argonne National Laboratory [email protected], [email protected],{tli13,dzhao8}@hawk.iit.edu,[email protected], [email protected].

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-05-08

Issue

Volume 4, Issue 6, May-June-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Revati. K., Padmashree. V., Veeresh Hiremath, Vinutha. D. C., Chandini. S. B., " Big Data Analytics With Business Intelligence: A Survey, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.434-437, May-June-2018.