A framework for Business Process Data Management based on Big Data Approach

In order to solve the problem of high cost and long cycle in the process of traditional subtractive material manufacturing of a complex-shaped mould, the technology of FDM rapid prototyping is used in combination with the global service idea of cloud manufacturing, where the information of various kinds of heterogeneous-forming process data produced in the process of FDM rapid prototyping is analysed. Meanwhile, the transfer and transformation relation of each forming process data information in the rapid manufacturing process with the digital model as the core is clarified, so that the FDM rapid manufacturing process is integrated into one, thus forming a digital and intelligent manufacturing system for a complex-shaped mould based on the cloud manufacturing big data management. This paper takes the investment casting mould of a spur gear as an example. Through research on the forming mechanism of jet wire, the factors affecting forming quality and efficiency is analysed from three stages: the pretreatment of the 3D model, the rapid prototyping, and the postprocessing of the forming parts. The relationship between the forming parameters and the craft quality is thus established, and the optimization schemes at each stage of this process are put forward through the study on the forming mechanism of jet wire. Through a rapid prototyping test, it is shown that the spur face gear master mould based on this technology can be quickly manufactured with a critical surface accuracy within a range of 0.036 mm–0.181 mm and a surface roughness within the range of 0.007–0.01 μm by only 1/3 the processing cycle of traditional subtractive material manufacturing. It lays a solid foundation for rapid intelligent manufacturing of products with a complex-shaped structure.

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Meta-modeling of Big Data management layer

International Journal of Emerging Trends in Engineering Research ◽

10.30534/ijeter/2019/01772019 ◽

2019 ◽

Vol 7 (7) ◽

pp. 36-43

Author(s):

Allae Erraissi ◽

Keyword(s):

Big Data ◽

Data Management ◽

Meta Modeling

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International Journal of Advanced Research in Big Data Management System

10.21742/ijarbms ◽

2020 ◽

Keyword(s):

Big Data ◽

Data Management ◽

Management System ◽

Data Management System

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Exploring Apache Spark Data APIs for Water Big Data Management

Advances in Intelligent Systems and Computing - Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) ◽

10.1007/978-3-030-11881-5_10 ◽

2019 ◽

pp. 105-117

Author(s):

Nassif El Hassane ◽

Hicham Hajji

Keyword(s):

Big Data ◽

Data Management ◽

Apache Spark

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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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