Data Evolution Analysis of Virtual DataSpace for Managing the Big Data Lifecycle

Author(s):  
Xin Cheng ◽  
Chungjin Hu ◽  
Yang Li ◽  
Wei Lin ◽  
Haolei Zuo
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.


Author(s):  
Artem A. Balyakin ◽  
Marina V. Nurbina ◽  
Sergey B. Taranenko
Keyword(s):  
Big Data ◽  

2019 ◽  
Vol 40 (25) ◽  
pp. 1995-1996 ◽  
Author(s):  
Lee Kamlet
Keyword(s):  
Big Data ◽  

2018 ◽  
Vol 45 (3) ◽  
pp. 322-340 ◽  
Author(s):  
Deepak Gupta ◽  
Rinkle Rani

The world is already into the information age. The huge growth of digital data has overwhelmed the traditional systems and approaches. Big data is touching almost all aspects of our life and the data-driven discovery approach is an emerging paradigm for computing. The ever-growing data provides a tidal wave of opportunities and challenges in terms of data capture, storage, manipulation, management, analysis, knowledge extraction, security, privacy and visualisation. Though the promise of big data seems to be genuine, still a wide gap exists between its potential and realisation. In last few years, there is a huge surge in research efforts in academia as well as industry to have a better understanding of big data. This article discusses the following: (1) big data evolution including a bibliometric study of academic and industry publications pertaining to big data during the period 2000–2017, (2) popular open-source big data stream processing frameworks and (3) prevalent research challenges which must be addressed to realise the true potential of big data.


2020 ◽  
Vol 11 (2) ◽  
pp. 396
Author(s):  
Ahmad Nurul FAJAR ◽  
Aldian NURCAHYO ◽  
Nunung Nurul QOMARIYAH

Nowadays, more and more people can enjoy fast internet access that can be used for various activities such as browsing, shopping online, video calls, playing games and so on. Businesses are also utilizing this very rapid increase in internet technology. They sell products and services through the internet with various attractive offers and competing with each other to increase their sales. One strategy that can be done to get more sales is through the method of personalizing services for customers. The personalization aspect in e-tourism has been predicted to increase. Customers who are making valuable data at every stage of their journey are making a challenge for travel companies to collect and link these data points to improve their customer experience. Learning the customer behaviour can be very significant for Online Travel Agent. Because collecting millions of search results through their services and provide a smart travel experience, Online Travel Agent in Indonesia must use Big Data and Cloud technology alignment to win the competition in the market. The entire data lifecycle must be simple because of the needs of users to keep batch ingesting a lot of data likes once in an hour. Streaming analytics has grown over the past few years, it has become one of the most critical components of most of the businesses. We proposed Online Travel Agent (OTA) for Tourism System Using Big Data and Cloud.


2021 ◽  
Vol 13 (24) ◽  
pp. 13827
Author(s):  
Seungjin Baek ◽  
Young-Gab Kim

Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security matters by examining the battlefield environment and the system through the flow of data in the battlefield. As such, this research was conducted to apply big data in the defense domain, which is a unique field. In particular, a three-layered system framework was designed to apply big data in the C4I system, which collects, manages, and analyzes data generated from the battlefield, and the security measures required for each layer were developed. First, to enhance the general understanding of big data and the military environment, an overview of the C4I system, the characteristics of the 6V’s, and the five-phase big data lifecycle were described. While presenting a framework that divides the C4I system into three layers, the roles and components of each layer are described in detail, considering the big data lifecycle and system framework. A security architecture is finally proposed by specifying security requirements for each field in the three-layered C4I system. The proposed system framework and security architecture more accurately explain the unique nature of the military domain than those studied in healthcare, smart grids, and smart cities; development directions requiring further research are described.


2019 ◽  
Vol 107 (12) ◽  
pp. 2294-2301 ◽  
Author(s):  
Bing Zhang ◽  
Zhengchao Chen ◽  
Dailiang Peng ◽  
Jon Atli Benediktsson ◽  
Bo Liu ◽  
...  

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