CFP last date
22 April 2024
Reseach Article

Big Data, Machine Learning and the BlockChain Technology: An Overview

by Francisca Adoma Acheampong
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 28
Year of Publication: 2018
Authors: Francisca Adoma Acheampong
10.5120/ijca2018916674

Francisca Adoma Acheampong . Big Data, Machine Learning and the BlockChain Technology: An Overview. International Journal of Computer Applications. 180, 28 ( Mar 2018), 1-4. DOI=10.5120/ijca2018916674

@article{ 10.5120/ijca2018916674,
author = { Francisca Adoma Acheampong },
title = { Big Data, Machine Learning and the BlockChain Technology: An Overview },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 180 },
number = { 28 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number28/29149-2018916674/ },
doi = { 10.5120/ijca2018916674 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:02:00.188140+05:30
%A Francisca Adoma Acheampong
%T Big Data, Machine Learning and the BlockChain Technology: An Overview
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 28
%P 1-4
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The importance of big data in machine learning cannot be overemphasized in recent times. Through the evolution of big data, most scientific technologies that relied heavily on enormous data in solving complex issues in human lives gained grounds; machine learning is an instance of these technologies. Various machine learning models that yield groundbreaking throughputs with high efficiency rates in predicting, detecting, classifying, discovering and acquiring in-depth knowledge about events that would otherwise be very difficult to ascertain have been made possible due to big data. Although big data has undoubtedly helped in the field of machine learning research ,over the years, its mode of acquisition has posed great challenge in industries,education and other agencies that obtained them for various purposes. This is because these large quantities of data cannot be stored on personal computers with limited storage capabicity but required the use of high storage capacity servers for effective storage. These servers may be owned by a group of companies or individuals who had the singular priviledge to modify the data in their possession as and when deemed relevant thus the creation of a centralized data storage environment. These were mostly refered to as the Third Parties (TP) in the data acquisition process. For the services they rendered, these trusted parties priced data in their possession expensively. The adverse effect is a limitation on various researches that could help solve a number of problems in human lives. It is worth mentioning that the security of these data being purchased expensively cannot be even assured limiting various researches that thrive on secured data. In order to curb these occurrences and have better machine learning models, the incorporation of Blockchain Technology databases into machine learning. This paper discusses the concept of big data, Machine Learning and Blockchains. It further discusses how Big data has impacted the Machine learning Community, the significance of Machine Learning and how the BlockChain Technology could be used similarly impact the Machine Learning Community. The aim of this paper is to encourge further research in incoporating the BlockChain Technology into Machine Learning.

References
  1. S. Athmaja, M. Hanumanthappa, and V. Kavitha. A survey of machine learning algorithms for big data analytics. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pages 1–4, March 2017.
  2. Nolan Bauerle. How does blockchain technology work? Available at:[url = https://www.coindesk.com/information/how-doesblockchain- technology-work/,, 2018. Accessed Feb 2018].
  3. Carla E Brodley and Mark A Friedl. Identifying mislabeled training data. Journal of artificial intelligence research, 11:131–167, 1999.
  4. C. Cachin. Blockchains and consensus protocols: Snake oil warning. In 2017 13th European Dependable Computing Conference (EDCC), pages 1–2, Sept 2017.
  5. Michael Crosby, Pradan Pattanayak, Sanjeev Verma, and Vignesh Kalyanaraman. Blockchain technology: Beyond bitcoin. Applied Innovation, 2:6–10, 2016.
  6. Arthur P Dempster, Nan M Laird, and Donald B Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the royal statistical society. Series B (methodological), pages 1–38, 1977.
  7. S. Gharatkar, A. Ingle, T. Naik, and A. Save. Review preprocessing using data cleaning and stemming technique. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pages 1–4, March 2017.
  8. Jiawei Han, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.
  9. T. M. Khoshgoftaar and P. J Rebours. Improving software quality prediction by noise filtering techniques. Comput Sci Technol, 22:387, 2007.
  10. Sotiris B Kotsiantis, I Zaharakis, and P Pintelas. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160:3–24, 2007.
  11. Sotiris B Kotsiantis, Ioannis D Zaharakis, and Panayiotis E Pintelas. Machine learning: a review of classification and combining techniques. Artificial Intelligence Review, 26(3):159–190, 2006.
  12. David J Lary, Amir H Alavi, Amir H Gandomi, and Annette L Walker. Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1):3–10, 2016.
  13. W. Meng, E. Tischhauser, Q. Wang, Y. Wang, and J. Han. When intrusion detection meets blockchain technology: A review. IEEE Access, PP(99):1–1, 2018.
  14. James Nechvatal. Public-key cryptography. Technical report, NATIONAL COMPUTER SYSTEMS LAB GAITHERSBURG MD, 1991.
  15. M. Ngxande, J. R. Tapamo, and M. Burke. Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques. In 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), pages 156–161, Nov 2017.
  16. Rod Pierce. What is data? Math Is Fun, Available at:[url = http://www.mathsisfun.com/data/data.html,, 2017. Accessed Feb 2018].
  17. A. Rathor and M. Gyanchandani. A review at machine learning algorithms targeting big data challenges. In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pages 1–7, Dec 2017.
  18. S. R. Suthar, V. K. Dabhi, and H. B. Prajapati. Machine learning techniques in hadoop environment: A survey. In 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), pages 1–8, April 2017.
  19. Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
  20. Karl Wst and Arthur Gervais. Do you need a blockchain? Cryptology ePrint Archive, Report 2017/375, 2017. https: //eprint.iacr.org/2017/375.
  21. X.Wu, X. Zhu, G. Q.Wu, andW. Ding. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1):97–107, Jan 2014.
  22. Li Xiang-wei and Qi Yian-fang. A data preprocessing algorithm for classification model based on rough sets. Physics Procedia, 25:2025–2029, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Big Data Machine Learning Blockchains Data Preprocessing