Some Best Practices in Big Data Management

Abstract Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.

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Privacy Preserving Framework for Big Data Management in Smart Buildings

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) ◽

10.1109/percomworkshops51409.2021.9430994 ◽

2021 ◽

Author(s):

Catherine Inibhunu ◽

Carolyn McGregor

Keyword(s):

Big Data ◽

Data Management ◽

Privacy Preserving ◽

Smart Buildings

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DaLiF: a data lifecycle framework for data-driven governments

Journal Of Big Data ◽

10.1186/s40537-021-00481-3 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Syed Iftikhar Hussain Shah ◽

Vassilios Peristeras ◽

Ioannis Magnisalis

Keyword(s):

Big Data ◽

Data Management ◽

Good Governance ◽

Business Community ◽

Data Driven ◽

Governmental Organizations ◽

Data Lifecycle ◽

The Government ◽

Data Ecosystem ◽

Governance Transparency

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.

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