Environmental Open Data in Urban Platforms: An Approach to the Big Data Life Cycle

This chapter focuses on the role of finance and the politics of collaboration, charting the development of the Digital Repository of Ireland (DRI). DRI have been beset with institutional politics concerning its framing, development, and operation. The future funding issue was just the latest example in a long list of fraught exchanges that could be traced back to its original conception and funding mechanism. The DRI was born out of a funding opportunity, but seemed destined to die due to a funding failure. Without a political solution, the data life cycle would turn full circle much more quickly than initially anticipated. Unless there is a means of covering the costs for labour, equipment and other essential inputs, data are not generated or stored, and thus cannot be used or shared. Even in open data projects, the data might be free to use but they were not free to create, or to process and host.

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Data Life Cycle Management in Big Data Analytics

Procedia Computer Science ◽

10.1016/j.procs.2020.06.042 ◽

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

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Big Data information governance by accountants

International Journal of Accounting and Information Management ◽

10.1108/ijaim-01-2017-0006 ◽

2018 ◽

Vol 26 (1) ◽

pp. 153-170 ◽

Cited By ~ 7

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.

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

10.22323/1.395.0938 ◽

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

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/1865/4/042088 ◽

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

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An Improved Security Threat Model for Big Data Life Cycle

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2018.7.1.1834 ◽

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.

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A Cross-Domain Comparative Study of Big Data Architectures

International Journal of Cooperative Information Systems ◽

10.1142/s0218843020300016 ◽

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.

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