Security model for Big Healthcare Data Lifecycle

Khaloufi, Hayat; Karim, Alamgir; Beni‐Hssane, Abderrahim; Saadi, Mostafa

doi:10.1016/j.procs.2018.10.199

Cited by 43 publications

(25 citation statements)

References 13 publications

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“…Big data is a concept that is characterized by data that have high volume, veracity, velocity, and variety. Big data needs economical, advanced ways of information processing to be used for generating insight and supporting decision making [21][22][23]. In datadriven organizations, big data is regarded as a critical strategic asset [24].…”

Section: Overview Of the Government Big Data Ecosystem And Data Lifecycle Fieldsmentioning

confidence: 99%

“…The other existing data lifecycles include: Data Lifecycle for HPC Scientific data perspective [114], Data lifecycle for cloud automation tools [40], Data Lifecycle for Telco networks data management [115], Energy big data lifecycle [116], Data lifecycle for the Tobacco industry [117], Data lifecycle for cloud computing [118];Data lifecycle for cloud data [119], Data lifecycle for IoTs [120], Personal data lifecycle [121], Data lifecycle about the coal mine industry [122], Data lifecycle for smart healthcare [123], Data Lifecycle Model for NSF [94], Data lifecycle cycle for smart cities [13], Storage data lifecycle [124], Research data lifecycle [125], lifecycle for CENS Data [126], data lifecycles for industry [127,128], a lifecycle for big scholarly data [129], a lifecycle for social and economic data [46], Data lifecycle for manufacturing [130], Research data lifecycle [131], a lifecycle for big healthcare data [23,132], data lifecycle [133], a lifecycle for environmental research data [134], a lifecycle for big data value creation [26], a lifecycle for big data analytics for psychologists [135], the information pyramid of Reynolds and Busby lifecycle [17], Yuri Demchenko data lifecycle [10], Data lifecycle [58], SCC-data lifecycle [44,54], Data value cycle [136], Lifecycle in databases [137], Knowledge process-lifecycle [138], CMM for Scienti...…”

Section: Rq1: Existing Data Lifecycle Models and Their Phasesmentioning

confidence: 99%

“…• Implement a strategy plan to select modernized extensible tools for public data collection platforms [58]. • Introduce a data protection awareness program while collecting data [23,70,132].…”

Section: Collection Phase Key Functions the Data Collection Phase Of Dalif Includes The Following Essential Functionsmentioning

confidence: 99%

“…• Identify noise and errors in the collected data and process this public data to remove such issues [61]. • Involve reliable and authorized HR resources in the phase to avoid the leakage of sensitive government data [23,121]. • Define filtering criteria to be used by the PAs and researchers to filter the public data, including research data as per their needs [61,62].…”

Section: Collection Phase Key Functions the Data Collection Phase Of Dalif Includes The Following Essential Functionsmentioning

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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Section: Overview Of the Government Big Data Ecosystem And Data Lifecycle Fieldsmentioning

confidence: 99%

Section: Rq1: Existing Data Lifecycle Models and Their Phasesmentioning

confidence: 99%

Section: Collection Phase Key Functions the Data Collection Phase Of Dalif Includes The Following Essential Functionsmentioning

confidence: 99%

Section: Collection Phase Key Functions the Data Collection Phase Of Dalif Includes The Following Essential Functionsmentioning

confidence: 99%

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DaLiF: a data lifecycle framework for data-driven governments

2021

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“…However, the capability of big data techniques to manipulate whatever the type of data allows the manipulation of those datasets efficiently. So, recent technological advances in the industry have improved their ability to handle this data, as well as developing secure frameworks [87][88][89]. The introduced solutions aim not only for treatment identification but also for improving the process of rendering healthcare.…”

Section: Healthcarementioning

confidence: 99%

Big Data: Analytical Solutions, Research Challenges and Trends

Ali¹,

Novikov²

2020

Proceedings of ISP RAS

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The term Big Data refers to an extensive collections of digital data generating every second. Produced datasets come in structured, semi-structured, and unstructured formats throughout the world, which is difficult for the traditional database management systems to analyze. Recently, big data analytics emerges as an essential research area due to the popularity of the Internet and the advent of new Web technologies. This growing area of research represents a multidisciplinary that attracts researchers from various research fields. Interested researchers are invited to design, develop, and implement several tools, technologies, architecture, and platforms for analyzing these large volumes of data. This paper begins with a brief introduction to big data and related concepts, including the main characteristics of big data, followed by discussions of the most significant open research challenges and emerging trends. Next, we review a study of big data analytics, the advantages of using big data solutions, and the preliminary assessments required before migrating from traditional solutions. Finally, we present a review of the recent main applications to obtain a broad perspective of big data analytics.

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Security and provenance for Internet of Health Things: A systematic literature review

Bai

Nazir

Bai

et al. 2021

J Software Evolu Process

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Internet of Health Things (IoHT) is an extension of the Internet of Things (IoT), which plays an important role in the observation, consultation, monitoring, and treatment process of remote exchange data processes in healthcare. The integration, computation, and interoperability are facilitated through various sensors, controllers, and actuators. Security is considered as one of the important factors for the communication of these devices to smoothly run the activities of healthcare. The current literature associated to the security of IoHT cover diverse aspects, though there is dire need of the knowledge, which can thoroughly show the review of current state‐of‐the‐art work in a systematic way. To overcome this limitation, the planned study offerings a systematic literature review (SLR) for the IoHT security and provenance. This review will support researchers to take benefit from the present literature and devise novel solutions considering the current study as evidence in IoHT. This paper focused on the security analysis and provenance for the IoHT and answering the defined questions in the current study.

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Security model for Big Healthcare Data Lifecycle

Cited by 43 publications

References 13 publications

DaLiF: a data lifecycle framework for data-driven governments

DaLiF: a data lifecycle framework for data-driven governments

Big Data: Analytical Solutions, Research Challenges and Trends

Security and provenance for Internet of Health Things: A systematic literature review

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