scholarly journals A Cross-Domain Comparative Study of Big Data Architectures

2020 ◽  
Vol 29 (04) ◽  
pp. 2030001
Author(s):  
Martin Macak ◽  
Mouzhi Ge ◽  
Barbora Buhnova
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Smart Cities ◽  
Application Domain ◽  
Domain Specific ◽  
Cross Domain ◽  
Data Life Cycle ◽  
Data Architecture ◽  
Practical Guidelines ◽  
Similarities And Differences

Nowadays, a variety of Big Data architectures are emerging to organize the Big Data life cycle. While some of these architectures are proposed for general usage, many of them are proposed in a specific application domain such as smart cities, transportation, healthcare, and agriculture. There is, however, a lack of understanding of how and why Big Data architectures vary in different domains and how the Big Data architecture strategy in one domain may possibly advance other domains. Therefore, this paper surveys and compares the Big Data architectures in different application domains. It also chooses a representative architecture of each researched application domain to indicate which Big Data architecture from a given domain the researchers and practitioners may possibly start from. Next, a pairwise cross-domain comparison among the Big Data architectures is presented to outline the similarities and differences between the domain-specific architectures. Finally, the paper provides a set of practical guidelines for Big Data researchers and practitioners to build and improve Big Data architectures based on the knowledge gathered in this study.

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

scholarly journals Data Life Cycle Management in Big Data Analytics

2020 ◽  
Vol 173 ◽  
pp. 364-371
Author(s):  
Kumar Rahul ◽  
Rohitash Kumar Banyal
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Life Cycle Management ◽  
Data Life Cycle

scholarly journals Big data technology in construction

2019 ◽  
Vol 97 ◽  
pp. 01032 ◽  
Author(s):  
Nikolay Garyaev ◽  
Venera Garyaeva
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Life Cycle ◽  
Construction Industry ◽  
Smart Cities ◽  
Entire Life ◽  
Entire Life Cycle ◽  
Intelligent Buildings ◽  
Big Data Technology

The article presents the results of the analysis of the use of large amounts of data in the construction industry, new trends such as BIM, IOT, cloud computing, intelligent buildings and smart cities with great prospects for application. These problems are related to the presence of huge amounts of data produced by the construction industry during the entire life cycle of a building, which are not fully used for optimizing processes and making decisions in construction.

Big Data information governance by accountants

2018 ◽  
Vol 26 (1) ◽  
pp. 153-170 ◽  
Author(s):  
Emily M. Coyne ◽  
Joshua G. Coyne ◽  
Kenton B. Walker
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Design Methodology ◽  
Decision Makers ◽  
Accounting Profession ◽  
Content Type ◽  
Data Life Cycle ◽  
Information Governance ◽  
And Control ◽  
Strong Ability

Purpose Big Data has become increasingly important to multiple facets of the accounting profession, but accountants have little understanding of the steps necessary to convert Big Data into useful information. This limited understanding creates a gap between what accountants can do and what accountants should do to assist in Big Data information governance. The study aims to bridge this gap in two ways. Design/methodology/approach First, the study introduces a model of the Big Data life cycle to explain the process of converting Big Data into information. Knowledge of this life cycle is a first step toward enabling accountants to engage in Big Data information governance. Second, it highlights informational and control risks inherent to this life cycle, and identifies information governance activities and agents that can minimize these risks. Findings Because accountants have a strong ability to identify the informational and control needs of internal and external decision-makers, they should play a significant role in Big Data information governance. Originality/value This model of the Big Data life cycle and information governance provides a first attempt to formalize knowledge that accountants need in a new field of the accounting profession.

scholarly journals German-Russian Astroparticle Data Life Cycle Initiative to foster Big Data Infrastructure for Multi-Messenger Astronomy

2021 ◽  
Author(s):  
Victoria Tokareva ◽  
Igor Bychkov ◽  
Andrey Demichev ◽  
Julia Dubenskaya ◽  
Oleg Fedorov ◽  
...  
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Life Cycle Initiative ◽  
Data Infrastructure ◽  
Data Life Cycle

scholarly journals MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

2020 ◽  
Vol 4 (3) ◽  
pp. 17 ◽  
Author(s):  
Suriya Priya R. Asaithambi ◽  
Ramanathan Venkatraman ◽  
Sitalakshmi Venkatraman
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Consumer Satisfaction ◽  
Smart Cities ◽  
Transportation Systems ◽  
Transport Systems ◽  
Hybrid Architecture ◽  
Smart Transportation ◽  
Transport Services ◽  
Data Architecture

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

scholarly journals The Model Design of Medical Data Life Cycle Based on Big Data Platform

2021 ◽  
Vol 1865 (4) ◽  
pp. 042088
Author(s):  
Ziqing Li ◽  
Shenglei Pei ◽  
Guiliang Feng
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Medical Data ◽  
Model Design ◽  
Data Life Cycle ◽  
Data Platform

scholarly journals An Improved Security Threat Model for Big Data Life Cycle

2018 ◽  
Vol 7 (1) ◽  
pp. 33-39
Author(s):  
Kanika . ◽  
Alka . ◽  
R. A. Khan
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Social Networking Sites ◽  
Point Of View ◽  
Security And Privacy ◽  
Security Threat ◽  
Threat Model ◽  
Data Life Cycle ◽  
Security Challenges ◽  
Data Lifecycle

Big data is a huge amount of data created by individuals related to their medical, internet activity, social networking sites, energy usage communication patterns etc. From these sources, data is being collected and processed by various survey organizations, national statistical agencies, medical centres, and other companies etc. There are many security challenges which occur during data transactions, such as un-authentication, phishing, Vishing, data mining based attacks, etc. From a security point of view the biggest challenge for big data is the protection of user’s privacy. Yazan et.al, have presented big data lifecycle threat model. This paper does a critical review of the work. An Improved Security Threat Model for Big Data Life Cycle has been proposed as a main contribution of the paper. A new phase i.e. data creation phase has been added to the life cycle and it is claimed that the phase is very important one with respect to security and privacy. To justify the claim theoretical and statistical evidences have been provided.

Sign in / Sign up

Export Citation Format

Share Document