Big Data Analytics With Business Intelligence: A Survey

Authors(5) :-Revati. K., Padmashree. V., Veeresh Hiremath, Vinutha. D. C., Chandini. S. B.

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”.

Authors and Affiliations

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

  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 .
  3. Art of scheduling for big data sciences.
  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. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} 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 kwang22@hawk.iit.edu, xzhou@hortonworks.com,{tli13,dzhao8}@hawk.iit.edu,mlang@lanl.gov, iraicu@cs.iit.edu.

Publication Details

Published in : Volume 4 | Issue 6 | May-June 2018
Date of Publication : 2018-05-08
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 434-437
Manuscript Number : CSEIT184681
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Revati. K., Padmashree. V., Veeresh Hiremath, Vinutha. D. C., Chandini. S. B., "Big Data Analytics With Business Intelligence: A Survey", International 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.
Journal URL : http://ijsrcseit.com/CSEIT184681

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