Data Life Cycle Management in Big Data Analytics

Rahul, Kumar; Banyal, Rohitash Kumar

doi:10.1016/j.procs.2020.06.042

Cited by 38 publications

(17 citation statements)

References 9 publications

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“…Data life cycle is an important theory to analyse the data process (Rahul and Banyal, 2020). Up to now, there were at least 55 different data life cycle models [Committee on Earth Observation Satellites (CEOS), 2021] and 12 of them were used in describing scientific data.…”

Section: A General Life Cycle Model For Scientific Datamentioning

confidence: 99%

Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

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Purpose This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data. Design/methodology/approach First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward. Findings The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data. Research limitations/implications As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders. Practical implications The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues. Originality/value This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

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Section: A General Life Cycle Model For Scientific Datamentioning

confidence: 99%

Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

View full text Add to dashboard Cite

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“…Share/Publish P9 Use [15,26,66,90,90,111,116,118,134,135,142], consumption [92] feedback [93] using [53], usage [10,48,119,133,143,146,148,153], Reuse [28,61,84,88,107], exploit [85,110], consume [114], application [130], use and re(use) [13,49,147], feedback [52,140], review [48,141] Use [58,116], destroy [111,143], Delete [13,66,108,124,148], dispose [51,113],termination [49]…”

Section: Approach To Dalifmentioning

confidence: 99%

“…In this phase, duplicated data, no longer required data, and useless data is removed from the system [50,58,88,111]. Data must be considered in terms of end of the usefulness of data or end of life [58,116].…”

Section: End Of Life Phasementioning

confidence: 99%

“…This data lifecycle consists of the following phases, plan, create, process & analysis, use, preserve, and access research data.OGD lifecycle In 2015, J. Attard et al mentioned an Open Government Data lifecycle[110]. This data lifecycle consists of the following phases, data creation, data selection, data analysis, data curation, data publishing, data discovery, data exploration, data storage, and data exploitation.Data Lifecycle for industrial and healthcare applications: In 2020, Kumar Rahul et al mentioned a data lifecycle for industrial and healthcare applications and focused on managing big data analytics[111]. This data lifecycle consists of the following phases, create, store, analysis, use, share, archive, and destroy.Web content management lifecycleIn 2003, S. McKeever, Dublin Institute of Technology, Ireland, defined Web Content Management (WCM) lifecycle [112].…”

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confidence: 99%

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DaLiF: a data lifecycle framework for data-driven governments

2021

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The 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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“…Protecting privacy is the practical need of human rights protection and the inevitable requirement of the development of information technology. Smart home privacy protection (SHPP) covers a wide range, involving every stage of the data life cycle (collection, use, retention, and disclosure) [5]. The smart home has prominent privacy characteristics, and the identification of private information depends on the specific application scenario.…”

Section: Introductionmentioning

confidence: 99%

The Development of Privacy Protection Standards for Smart Home

Liu¹,

Yang

et al. 2022

Wireless Communications and Mobile Computing

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With the rapid development of network technology, society has entered the Internet of Things (IoT) era. Enriching IoT devices enable smart home privacy information to be easily obtained through data mining. Smart home privacy protection (SHPP) has a farreaching impact on the development of all aspects of society. Industrial and academic circles pay great attention to the privacy protection of the smart home, and the ability to protect the privacy needs to be improved. SHPP standards are one way to evaluate privacy protection capabilities at this stage. Based on our research on privacy protection standards, this paper summarizes the development of privacy protection standards for the first time. It divides them into three phases: creation, exploration, and expansion. According to the characteristics of the privacy data rotation, the SHPP standard system is proposed, including basic commonality, critical technology, auxiliary management, test and certification, and device application. Finally, we focus on the opportunity and challenges of smart home privacy security standards brought by the current big data and prospect the trend of privacy protection technologies and standard study. They provide a connection for subsequent standard investigations.

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Data Life Cycle Management in Big Data Analytics

Cited by 38 publications

References 9 publications

Process-driven quality improvement for scientific data based on information product map

Process-driven quality improvement for scientific data based on information product map

DaLiF: a data lifecycle framework for data-driven governments

The Development of Privacy Protection Standards for Smart Home

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