Conceptual Modeling of Big Data Extract Processes with UML

Martinez-Mosquera, Diana; Luján‐Mora, Sergio

doi:10.1109/inciscos.2017.18

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The UML class diagram described the static relations among different classes as the structure of the information and the behavior of the system. Martinez et al [4], promoted the use of UML for modeling the extraction process of Big Data specifically. This work is an approach for modeling the Extract Transform Load (ETL) process in a data warehouse, for depicting the data conversion and the loader process involved in the extracted task.…”

Section: C4-4mentioning

confidence: 99%

UML to model pollution patterns of the Smart Cities for Metropolitan Zone of Guadalajara

Martínez Vargas,

Estrada Guzmán,

Arellano

2021

recibe

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La contaminación en poblaciones densas como la Zona Metropolitana de Guadalajara crece exponencialmente, afectando la salud de los ciudadanos y reduciendo su Calidad de Vida. Uno de los principales retos de investigación que se persiguen con el estudio de las Smart Cities es la contaminación ambiental para mejorar el bienestar de los ciudadanos y proteger los espacios naturales. Por lo tanto, es urgente el desarrollo de Tecnologías de la Información para reducir este problema mediante el análisis científico de datos, para clasificar las zonas con mayor contaminación. Actualmente, estos datos son capturados por estaciones de monitoreo constante, generando un gran volumen de información que representa un desafío para el proceso de clasificación. Este trabajo propone un modelo UML (Lenguaje Unificado de Modelado) para la ejecución automática de un algoritmo de clasificación utilizando Máquinas de Vectores de Soporte con la implementación de librerías en python para procesamiento paralelo. Como resultado de esta investigación, se desarrolló un modelo UML para identificar patrones de contaminación en Ciudades Inteligentes, por ejemplo, la Zona Metropolitana de Guadalajara.

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Section: C4-4mentioning

confidence: 99%

UML to model pollution patterns of the Smart Cities for Metropolitan Zone of Guadalajara

Martínez Vargas,

Estrada Guzmán,

Arellano

2021

recibe

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“…In [10], the authors propose the use of UML (Unified Modelling Language) standard for modelling the big data extract process at a conceptual level with the use of new specific stereotypes proposed by the UML deployment diagrams and other using the approach of the ETL (Extract, Transform, Load) process in data warehouses. The paper presents case studies based on three tools used in the extraction process, Sqoop, Flume and Data Click.…”

Section: Research Background and Related Workmentioning

confidence: 99%

Big Data Management and Analytics Metamodel for IoT-Enabled Smart Buildings

et al. 2020

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Big data management and analytics, in the context of IoT (Internet of Things)-enabled smart buildings, is a challenging task. It is a diffused and complex area of knowledge due to the diversity of IoT devices and the nature of data generated by the IoT devices. Many international bodies have developed metamodels for IoT-enabled ecosystems to allow knowledge sharing. However, these are often narrow in focus and deal with only the IoT aspects without taking into account the management and analytics of big data generated by the IoT devices. Hence, in this paper we propose a metamodel for the Integrated Big Data Management and Analytics (IBDMA) framework for IoT-enabled smart buildings. The IBDMA Metamodel can be used to facilitate interoperability between existing big data management and analytics ecosystems deployed in smart buildings or other smart environments. We import the metamodel into a knowledge graph management tool and by considering a case study we validate the metamodel using this tool. The evaluation results demonstrate that IBDMA Metamodel is indeed suitable for its intended purpose.

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