A New Architecture for Cognitive Internet of Things and Big Data

Sassi, Mohamed Saifeddine Hadj; Ghozzi, Faiza; Fourati, Lamia Chaari

doi:10.1016/j.procs.2019.09.208

Cited by 35 publications

(5 citation statements)

References 19 publications

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“…Consequently, the technology that comprises these solutions could play a significant role in business information architecture. The process of data flow includes data source, data collection, ETL, Business Analytics, and Service (Hadj Sassi, Jedidi, & Fourati, 2019). The architecture components of the big data ecosystem are defined in Demchenko et al (2014).…”

Section: Big Data Architecturementioning

confidence: 99%

The Analysis of Big Data Architecture for Healthcare Service, a Case Study of a Public Hospital

Noonpakdee

2022

IJMKL

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Big data is one of the promising technologies which helps the healthcare sector improve patient experience, reduce the cost of treatment, predict epidemics, and provide competitive healthcare services. This research aims to study and analyse big data architecture for a public hospital in Thailand.Study design/methodology/approach: This research employs a qualitative approach. For data collection and analysis, document analysis is applied as a qualitative method to obtain empirical data. In addition, in-depth interviews of the IT manager, doctor, and staff are conducted to confirm the big data architecture and examine issues in a case study public hospital.Findings: According to the study, big data architecture for the public hospital comprises data acquisition, data integration, data analytics, data visualisation, a health data centre on a cloud, and data governance.Originality/value: The architecture components and analysis can be applied as a guideline for big data processing and analytics to create a solution for implementing big data in healthcare effectively.

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Section: Big Data Architecturementioning

confidence: 99%

The Analysis of Big Data Architecture for Healthcare Service, a Case Study of a Public Hospital

Noonpakdee

2022

IJMKL

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“…The cognitive network capabilities will be available in the network layer. This is attained by providing cognitive wireless network [70] with infrastructure as well as with ad-hoc cognitive network [71]. Both these cognitive capabilities work together and forms a stabilized cognitive network.…”

Section: Internationalmentioning

confidence: 99%

Developmental Proposal of a Generic Architecture for Cognitive Internet of Things

Jose,

Mathew

2022

IJSRCSE

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Cognitive science is developing in a faster pace and finding applications in science and technology. A cognitive network is a communication network that uses cognitive processes to understand present situations, make decisions based on its findings and take feedback from those decisions to take corrective actions. Cognitive computing uses computer models for simulating the human thought process. The Internet of Things (IoT) consists of "Things" which are networked for computation to take actions accordingly. The development of a cognitive network as well as that of cognitive computing can lead to the development of IoT that is cognitive. In this paper, we propose an architecture for cognitive IoT that is derived from cognitive network and cognitive computing.

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“…IoT refers to the network of interconnected every-day items that are often equipped with ubiquitous intelligence [61,62]. The Internet of Things (IoT) is the inter-related system of physical computing devices that can be accessed via the Internet [63].…”

Section: Internet Of Things (Connectedness)mentioning

confidence: 99%

“…Human sensors, user inputs, documents, environment sensors, localization, motion, and actuators [61,62] are data inputs for many smart devices.…”

Section: Sensors and Actuatorsmentioning

confidence: 99%

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

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Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

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A New Architecture for Cognitive Internet of Things and Big Data

Cited by 35 publications

References 19 publications

The Analysis of Big Data Architecture for Healthcare Service, a Case Study of a Public Hospital

The Analysis of Big Data Architecture for Healthcare Service, a Case Study of a Public Hospital

Developmental Proposal of a Generic Architecture for Cognitive Internet of Things

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

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