Pipeline-Based Linear Scheduling of Big Data Streams in the Cloud

Tantalaki, Nicoleta; Souravlas, Stavros; Roumeliotis, Μanos; Katsavounis, Stefanos

doi:10.1109/access.2020.3004612

Cited by 21 publications

(12 citation statements)

References 37 publications

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“…In the whole cultural heritage protection system in China, protection planning plays an extremely important role. To sum up, protection planning is a plan that takes coordinated protection and construction development as the starting point, determines the protection principles, contents and priorities of historical and cultural heritage, and puts forward protection measures as the main content [5][6].…”

Section: Protection Of Ahmentioning

confidence: 99%

Big Data Technology Applied to the Architectural Heritage Protection

Che¹

2023

Proceedings of the 2nd International Conference on Information, Control and Automation, ICICA 2022, December 2-4, 2022, Chongqi

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China is a large country of world cultural heritage, with a rich number of architectural heritage(AH) of various types. With the continuous expansion of the objects of AH protection(AHP), the number of AH to be protected is also increasing, and the protection work is urgent. Under the current policy and system, conservation planning is the main means of protection, and the preparation of AH conservation planning needs to be based on detailed information, so high-precision acquisition of current information is crucial. This paper studies and analyzes the application of big data in the protection of AH, analyzes the analysis process framework of big data in the protection and management of AH, discusses the development of strategies for the protection of AH assisted by big data technology(BDT) achievements; Through the analysis of the strategy of building heritage protection assisted by BDT, this paper shows that within the scope of building heritage protection, the use of big data technology can conduct high-density real-time monitoring of building heritage, provide support for enriching the technical means of building heritage protection planning and enhancing its scientificity and rationality, which is of great significance for the protection of future building heritage.

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Section: Protection Of Ahmentioning

confidence: 99%

Big Data Technology Applied to the Architectural Heritage Protection

Che¹

2023

Proceedings of the 2nd International Conference on Information, Control and Automation, ICICA 2022, December 2-4, 2022, Chongqi

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“…We suggest that future studies consider academic databases, books and English language and non-English language journals for a systematic literature review on a larger scale to study quality models in more detail. In addition, more research ought to be conducted with the help of scientometrics, document analysis, text analysis, text classification and bibliometrics, as well as with big datasets using algorithms related to pipeline-based linear scheduling of big data streams in the cloud [193][194][195][196][197][198][199][200][201].…”

Section: Conclusion Limitations and Agenda For Future Studiesmentioning

confidence: 99%

Sustainable Development and Implementation of Quality Management Excellence Models in Public Organizations: A Systematic Literature Review

Taraza,

Anastasiadou,

Masouras

et al. 2023

Sustainability

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Purpose: The purpose of this study was to determine the effects of the European Foundation for Quality Management Excellence Model and Six Sigma and Lean Six Sigma approaches in public organizations. Design/methodology/approach: A systematic literature review was conducted based on articles from three academic publishers (Emerald, Elsevier/Science Direct and Taylor & Francis). The 88 selected journal articles were published between 2004 and 2022 and documented the results of the quality tools. Findings: The effects of applying the models in the public sector are presented. From the literature review, specific findings were identified regarding the motivations of all areas of education and services and the challenges they face in applying the qualitative tool methodologies. The main topics discussed are the human factors involved in implementing quality tools. Research limitations/implications: An important limitation is that data were drawn from only three major journals and the authors did not always have access to all databases and peer-reviewed journals or to any review articles in languages other than English. Multiple keywords limited the article search, as qualitative tools have been widely used in the private sector but less so in the public sector. Practical implications: The results and limitations detailed in the study and presentation of the 88 articles will motivate academic researchers to further study the application of qualitative tools in the public sector and fill the knowledge gap caused by the limited publications on this topic. Originality/value: The European Foundation for Quality Management Excellence Model and the Six Sigma and Lean Six Sigma approaches have not been widely implemented in the public sector, and literature reviews are limited despite the increasing trend of their use in the sector in recent years. More future research in public administration is needed to determine the effects and limitations of implementing qualitative tools.

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“…Souravlas [38] addressed the problem of the balanced data flow among data centers. Tantalaki et al [39] introduced a pipeline-based linear scheduling approach for big data streams. Lattuada et al [40] presented a resource-allocation approach for big data analytics.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

Chung

Lee

et al. 2020

Applied Sciences

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Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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Pipeline-Based Linear Scheduling of Big Data Streams in the Cloud

Cited by 21 publications

References 37 publications

Big Data Technology Applied to the Architectural Heritage Protection

Big Data Technology Applied to the Architectural Heritage Protection

Sustainable Development and Implementation of Quality Management Excellence Models in Public Organizations: A Systematic Literature Review

Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

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