Big Data Processing Technologies in Distributed Information Systems

Shakhovska, Nataliya; Boyko, Nataliya; Zasoba, Yevgen; Beňová, Eleonóra

doi:10.1016/j.procs.2019.11.047

Cited by 23 publications

(7 citation statements)

References 16 publications

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“…Oleh karena itu, diperlukan metode yang tepat untuk mengirimkan perubahan ke pengguna agar tidak mengganggu layanan yang sedang berjalan. Dalam pemrosesan Big Data, kita tidak bisa melakukannya secara konvensional [1]. Hal tersebut didukung dengan beberapa variabel yang telah teridentifikasi, yaitu (1) volume, (2) velocity, (3) variety, (4) veracity [2].…”

Section: Pendahuluanunclassified

Implementasi CI/CD Pada Microservices Untuk Meningkatkan Availability Pada Pemrosesan Big Data

Sukadharma,

Suputra

2024

JLK

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Big Data Processing needed a reliable system that not let the data loss. But sometimes we need to make the system down for a while because we need to push the newest changes of the system. The automation will help us achieve that. Continuous Integration and Continuous Deployment help us to reduce the downtime and increase the availability of the system. Thus, the implication will be led to reduce of data loss. This research focusses on the implementation of CI/CD Pipeline on single-pointof-failure service on Microservices Architecture. This research use Load-Testing to measure data loss on certain amount of time. The result on this research show that implementing CI/CD Pipeline on the Microservices that we made, make the down time will be less than 45 Second with 20 Virtual user who send the data.

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Section: Pendahuluanunclassified

Implementasi CI/CD Pada Microservices Untuk Meningkatkan Availability Pada Pemrosesan Big Data

Sukadharma,

Suputra

2024

JLK

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“…The novelty of this software lies not only in the fact that it extracts various types of data from variety of sources, including websites and software programs, but also in the fact that it handles all necessary transformation, integration, cleaning, normalization, and structuring operations. Other tools were developed for different purposes [41] such as extracting hashtags and emoticons from tweets and classifying them into different sentiments, [42] clustering text documents in the cloud using the growing hierarchical self-organizing maps algorithm, [43] using a heuristic algorithm for automatic navigation and information extraction from a journal's home page, [44] providing a platform for big data in medical domain analysis, and [45] enhancing probabilistic consistency-based multiple sequence alignment (ProbCons) for multiple sequence alignment in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Big Data Bot with a Special Reference to Bioinformatics

Al-Omari¹,

Tawalbeh²,

Akkam³

et al. 2023

Computers, Materials &Amp; Continua

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There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of data from web-based (point-and-click) resources or graphical user interfaces that cannot be accessed using command-line tools. The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides (AMP) sequences (46240 hits) from various MARVIN software panels, which can be later utilized to develop novel AMPs. Furthermore, for machine learning research, the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank. As a result, data collection from the web will become faster and less expensive, with no need for manual data extraction. The software is critical as a first step to preparing large datasets for subsequent stages of analysis, such as those using machine and deep-learning applications.

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“…), sin un control de salida definido y como información no estructurada. Y es esta última característica de datos la que ha despertado especial interés en la comunidad científica en el desarrollo de estudios y búsqueda de patrones estándar del comportamiento de la información de salida e interpretación y análisis de los mismos, (Shakhovska et al, 2019).…”

Section: Implicaciones Electrónicasinteligencia Clínicaunclassified

Big Data y las implicaciones en la cuarta revolución industrial - Retos, oportunidades y tendencias futuras

González

Vásquez

Uribe

et al. 2021

RVG

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La cuarta revolución industrial se presenta, en la actualidad, como un proceso de transformación y evolución productiva. El comportamiento globalizado de los mercados, las nuevas tendencias tecnológicas y el auge de innovadoras metodologías, han transformado la industria en un espacio de interacción interdisciplinar para la toma de decisiones organizacionales. Uno de estos espacios es la generación, obtención y análisis de los datos obtenidos de acuerdo con la relación hombre-máquina, también conocido en la actualidad como el Big Data. El presente artículo de investigación descriptiva tiene por objetivo exponer escenarios de aplicación, control y análisis de técnicas, tecnologías y metodologías asociados al Big Data en tres sectores principales: salud, financiero y transporte y logística. Los resultados obtenidos permiten evidenciar el impacto y relevancia con relación al mundo Big Data y sus aplicaciones en los sectores industriales de estudio como eje de reflexión y repercusiones profesionales. Se concluye que la integración de hardware y software en el campo del Big Data se hace indispensable en pro de la mejora de la calidad de servicio de teleasistencia en los pacientes, así como en los servicios logísticos y financieros

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Big Data Processing Technologies in Distributed Information Systems

Cited by 23 publications

References 16 publications

Implementasi CI/CD Pada Microservices Untuk Meningkatkan Availability Pada Pemrosesan Big Data

Implementasi CI/CD Pada Microservices Untuk Meningkatkan Availability Pada Pemrosesan Big Data

Big Data Bot with a Special Reference to Bioinformatics

Big Data y las implicaciones en la cuarta revolución industrial - Retos, oportunidades y tendencias futuras

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