research-article

The 51 V's Of Big Data: Survey, Technologies, Characteristics, Opportunities, Issues and Challenges

Authors:

Mohammad Rashid Hussain,

Quadri Noorulhasan Naveed,

Shamimul QamarAuthors Info & Claims

COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

Pages 19 - 24

https://doi.org/10.1145/3312614.3312623

Published: 05 May 2019 Publication History

Abstract

Currently Big Data is the biggest buzzword, and definitely, we believe that Big Data is changing the world. Some researchers say Big Data will be even bigger buzzword than the Internet. With fast-growing computing resources, information and knowledge a new digital globe has emerged. Information is being created and stored at a fast rate and is being accessed by a vast range of applications through scientific computing, commercial workloads, and social media. In 2018, over 28 billion devices globally, are connected to the internet. In 2020, more than 50 billion smart appliances will be connected worldwide and internet traffic flow will be 92 times greater than it was in 2005. The usage of such a massive number of connected devices not only increase the data volume but also the velocity of data addition with speed of light on fiber optic and various wireless networks. This fast generation of enormous data creates numerous threats and challenges. There exist various approaches that are addressing issues and challenges of Big Data with the theory of Vs such as 3 V's, 5 V's, 7 V's etc. The objective of this work is to explore and investigate the status of the current Big Data domain. Further, a comprehensive overview of Big Data, its characteristics, opportunities, issues, and challenges have been explored and described with the help of 51 V's. The outcome of this research will help in understanding the Big Data in a systematic way.

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Khan MKhan M(2024)Applications of Artificial Intelligence in Sustainable Supply Chain Management in the Medical SectorAI Applications for Business, Medical, and Agricultural Sustainability10.4018/979-8-3693-5266-3.ch002(23-46)Online publication date: 29-Mar-2024
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Index Terms

The 51 V's Of Big Data: Survey, Technologies, Characteristics, Opportunities, Issues and Challenges
1. Information systems
  1. Data management systems
    1. Database design and models

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COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

May 2019

241 pages

ISBN:9781450366403

DOI:10.1145/3312614

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 May 2019

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COINS '19

COINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS

May 5 - 7, 2019

Crete, Greece

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https://doi.org/10.48084/etasr.7339
Khan MKhan M(2024)Applications of Artificial Intelligence in Sustainable Supply Chain Management in the Medical SectorAI Applications for Business, Medical, and Agricultural Sustainability10.4018/979-8-3693-5266-3.ch002(23-46)Online publication date: 29-Mar-2024
https://doi.org/10.4018/979-8-3693-5266-3.ch002
Dhabliya DVeeraiah VDari SKumar JDhabliya RPramanik SGupta A(2024)Creating a Data Lakehouse for a South African Government-Sector Learning Control Enforcing Quality Control for Incremental Extract-Load-Transform PipeBig Data Quantification for Complex Decision-Making10.4018/979-8-3693-1582-8.ch004(88-109)Online publication date: 31-May-2024
https://doi.org/10.4018/979-8-3693-1582-8.ch004
Feretzakis GSakagianni AAnastasiou AKapogianni ITsoni RKoufopoulou CKarapiperis DKaldis VKalles DVerykios V(2024)Machine Learning in Medical Triage: A Predictive Model for Emergency Department DispositionApplied Sciences10.3390/app1415662314:15(6623)Online publication date: 29-Jul-2024
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