article

SmartData 4.0: a formal description framework for big data

Authors:

Morteza Sargolzaei Javan,

Mohammad Kazem AkbariAuthors Info & Claims

The Journal of Supercomputing, Volume 75, Issue 7

Pages 3585 - 3620

https://doi.org/10.1007/s11227-018-2705-y

Published: 01 July 2019 Publication History

Abstract

Describing big data problems and solutions in a formal language can accelerate the innovation and development across many sectors to launch smarter services and applications from data. SmartData 4.0 provides a framework to provide metadata and relations in a formal language. It could also be considered as a technique that empowers raw data by wrapping in a cloak of intelligence. From linear regression to more complex mathematical models, the SmartData Description Framework enables us to define context-aware behaviors linked to data. The framework also supports formalized description of data operations such as data fusion, transformation, and provenance management. We have shown some practical examples step by step, during the whole formalization process.

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Cited By

Wang TXu Q(2024)Research on safety verification methods of static data of train control systems based on deep association rulesThe Journal of Supercomputing10.1007/s11227-024-05948-780:9(13124-13140)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-05948-7
Xu DGao YTu TXiao X(2022)A Big Data Integration Platform for Ideological and Political Education for Smart CampusesSecurity and Communication Networks10.1155/2022/59739202022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5973920
Chen RLi ZLai Y(2021)Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear AnalysisComplexity10.1155/2021/84788682021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8478868

Index Terms

SmartData 4.0: a formal description framework for big data
1. Information systems
  1. Information retrieval

Index terms have been assigned to the content through auto-classification.

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cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 75, Issue 7

July 2019

628 pages

ISSN:0920-8542

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Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2019

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Cited By

Wang TXu Q(2024)Research on safety verification methods of static data of train control systems based on deep association rulesThe Journal of Supercomputing10.1007/s11227-024-05948-780:9(13124-13140)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-05948-7
Xu DGao YTu TXiao X(2022)A Big Data Integration Platform for Ideological and Political Education for Smart CampusesSecurity and Communication Networks10.1155/2022/59739202022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5973920
Chen RLi ZLai Y(2021)Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear AnalysisComplexity10.1155/2021/84788682021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8478868

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