Big Data Tools in Processing Information from Open Sources

Petrova, Mariana; Сущенко, Олена; Trunina, Iryna; Dekhtyar, Nadiya

doi:10.1109/saic.2018.8516800

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…Diastema is divided into several components, each performing a specific task (Figure 1). where the dataset description can be enriched through metadata fields, and finally uploaded on the MinIO [10]. During the Process Graph component, a user-friendly interface, using LeaderLine library [10], provides the necessary tools to build a custom graph based on the dataset needs.…”

Section: Methodsmentioning

confidence: 99%

“…where the dataset description can be enriched through metadata fields, and finally uploaded on the MinIO [10]. During the Process Graph component, a user-friendly interface, using LeaderLine library [10], provides the necessary tools to build a custom graph based on the dataset needs. This results in a flexible drag and drop node system, where the nodes can be connected to declare the process flow through a graph.…”

Section: Methodsmentioning

confidence: 99%

“…The next component includes the Analytics Services, a collection of three (3) sub-components (Data Loading, Data Cleaning, Spark Engine). A typical use case involves a serial execution of these subcomponents, starting with the data collection processed by the Data Loading, and its further enhancement through the Data Cleaning, before proceeding with the model's training through the Spark Engine [10]. For the scaling of the Diastema end-users, a Computing Cluster has been installed using Kubernetes [10], deposited into a private digital cloud to ensure data confidentiality.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

Kiourtis

Karamolegkos

Karabetian

et al. 2022

Studies in Health Technology and Informatics

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Big Data has proved to be vast and complex, without being efficiently manageable through traditional architectures, whereas data analysis is considered crucial for both technical and non-technical stakeholders. Current analytics platforms are siloed for specific domains, whereas the requirements to enhance their use and lower their technicalities are continuously increasing. This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, namely Diastema, as a collection of efficient and scalable components, offering user-friendly analytics through graph data modelling, supporting technical and non-technical stakeholders. Diastema’s applicability is evaluated in healthcare through a predicting classifier for a COVID19 dataset, considering real-world constraints.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

Kiourtis

Karamolegkos

Karabetian

et al. 2022

Studies in Health Technology and Informatics

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“…The presented standpoints are only a few among a myriad of definitions formulated by a large group of scientists who have dealt with this issue. In this field we can distinguish pieces of work Pukala [7,9], Pukala&Petrova [8], Kurmanov et al [10], Seitzhanov et al [11], Mussapirov et al [12], Petrova et al [13], whose multi-layered analyses of innovation have enriched and expanded knowledge in this scope.…”

Section: Start-ups As Innovative Enterprisesmentioning

confidence: 99%

The Impact of IT Risks on the Development of Innovative Start-Up of Mining Enterprises

Pukała

Linde

2020

E3S Web Conf.

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This study aims at presenting IT risks, the materialization of which can have impact on business activity of innovative enterprises, including start-ups. We need to emphasize that such enterprises are largely associated with the use and implementation of modern technologies in various sectors of the economy. We also need to underline that enterprises of this type to an ever-greater extent act as an impulse triggering innovativeness of the economy as seen from the national and international perspective. Therefore, risk limitation in the enterprise development process, especially in the field of IT, may be an element that supports reaching the product scaling stage and working out an optimal business model, which stands as a chance of achieving market success and competitive advantage. As indicated by researchers, start-ups identify IT risks within the area of their activity and undertake actions aimed at limiting them.

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“…Modern technologies aimed at improving algorithms and methods of data collection, storage and processing are tailoring the quality of output information for the end user, including the development of artificial intelligence systems, whose analytical modules and methods are used to filter out unnecessary information and search for dependencies between socio-economic processes (Dyachenko at al, 2018;Dellis et al, 2016;Nenkov et al, 2016;Petrova et al, 2018;Nenkov et al, 2017;Tvaronavičienė, 2017;Tumalavičius et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Business scaling through outsourcing and networking: selected case studies

Mussapirov¹,

Jalkibaeyev²,

Kurenkeyeva³

et al. 2019

JESI

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A business scaling on the basis of outsourcing and creation of an entrepreneurial network is discussed in this article.The network allows to expand the resource capabilities of its member enterprises. This aspect seems significant in the case of the inadequacy of researches that consider the period of the emergence of network structures. The proposed nanocorporation model assumes a network of independent, specialized supplier companies with a small coordinating integrator. In nanocorporations, the core company outsources most of its functions and develops its main competency, specializing in planning, corporate management, and marketing. An expanding affiliate business network provides fast business growth, minimization of costs, high mobility. The article examines the process of creating an entrepreneurial network using the example of three Kazakhstani small and medium-sized businesses. On the basis of the analysis, the stages of the decision-making sequence are proposed while creating a network. Case study method is used as the main one in the work.

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Big Data Tools in Processing Information from Open Sources

Cited by 19 publications

References 9 publications

An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

The Impact of IT Risks on the Development of Innovative Start-Up of Mining Enterprises

Business scaling through outsourcing and networking: selected case studies

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