REVIEW ON ARTIFICIAL BEE COLONY ALGORITHM ON BIG DATA TO FIND OUT REQUIRED DATA SOURCES

Shaleen Shukla, Mr. Rutvik Mehta

Abstract


Big data is a large amount of data which is hard to handle by on hand systems. It requires new structures, algorithms and techniques. As data increases as per volume, dark data also will increase. Artificial Bee Colony algorithm is a part of Swarm Intelligence. It is based on how honey bees are working to find out their food sources. In Big Data there is distributed environment so required sources may be on different places. During process the data these data sources have to find out from different places and analyze a one system. This requires calculation which can help us to find out best option for our required data sources. ABC algorithm is used to overcome limitations of ant colony algorithm. In ant colony initialization will be repeat from starting point in case of failure. In bee colony optimization initialization happens only once. It is used to find out required data source based on parameters out of multiple data sources.  Thus, artificial bee colony algorithm can be used to find out best data sources. We can store these derived data sources on cloud for further processing. Bee colony algorithm generally used in data mining and networking field. It can be used for Big Data for identifying data resources.

Keywords


Ant colony optimization; Bee colony optimization; Distributed data sources

Full Text:

PDF

References


Researh Papers

“Applications of Artificial Bee Colony Optimization Technique: Survey”, Kuldeep Singh Kaswan, Sunita Chaudhari, Kapil Sharma, 978-9-3805-4416-8/15/$31.00 c 2015 IEEE

“Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud”, K.R. Remesh Babu and Philip Samuel, Springer International Publishing Switzerland 2016

“The rise of “big data” on cloud computing: Review and open research issues”, Ibrahim Abaker Targio Hashem, Ibrar Yaqoob, Nor Badrul Anuar,Salimah Mokhtar , Abdullah Gani, Samee Ullah Khan, Information Systems 47 (2015) 98–115

“Cluster based wireless sensor network routing using artificial bee colony algorithm Dervis Karaboga, Selcuk Okdem, Celal Ozturk, Springer Science+Business Media, LLC 2012

“Optimization of the Running Speed of Ant Colony Algorithm with Address-based Hardware Method”, ElnazShafighFard, Khalil Monfaredi, ISSN: 2180 - 1843 Vol. 7 No. 1 January - June 2015

“Distributed Virtualization Manager for KVM Based Cluster”, Mr. Uchit Gandhi, Mr. Mitul Modi, Ms. Mitali Raval, Mr. Paavan Maniar, Dr. Narendra Patel, Prof Kirti Sharma, Procedia Computer Science 79 ( 2016 ) 182 – 189, ScienceDirect

“Data Model for Big Data in Cloud Environment”, Imran Khan, S. K. Naqvi, Mansaf Alam, S. N. A Rizvi

“Research of Resource Allocation in Cloud Computing Based on

Improved Dual Bee Colony Algorithm”, Wu Ju-Hua, International Journal of Grid Distribution Computing, Vol. 8, No.5, (2015), pp.117-126

“SAACO: A Self Addictive Ant Colony Optimization in Cloud Computing”, Weifeng Sun, Zhenxing Ji, Jianli Sun, Ning Zhang, Yan Hu, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing

”Unsupervised probabilistic feature selection using ant colony optimization”, Behrouz Zamani Dadaneh∗, Hossein Yeganeh Markid, Ali Zakerolhosseini, Expert Systems With Applications 53 (2016)

Websites

https://en.wikipedia.org/wiki/Artificial_bee_colony_algorithm

https://en.wikipedia.org/wiki/Swarm_intelligence

` [13] http://mf.erciyes.edu.tr/abc/index.htm

https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms


Refbacks

  • There are currently no refbacks.




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

Copyright © 2021 INTERNATIONAL EDUCATION AND RESEARCH JOURNAL