A novel transversal processing model to build environmental big data services in the cloud

Barron-Lugo, J. Armando; Gonzalez-Compeán, J. L.; Carretero, J.; López-Arévalo, Iván; Montella, Raffaele

doi:10.1016/j.envsoft.2021.105173

Cited by 5 publications

(3 citation statements)

References 51 publications

(69 reference statements)

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“…These works take advantage of the ability of the cloud to scale resources according to their demand 22 . Barron‐Lugo et al 23 present a model to process environmental data in the cloud. Their model benefits from this scalability to enable the deployment of multiple processing structures to process the data.…”

Section: Related Workmentioning

confidence: 99%

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

Mellone,

De Vita,

Sánchez‐Gallegos

et al. 2023

Concurrency and Computation

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SummaryCloud‐based services have proved useful in several research fields, such as engineering, health science, and astrophysics, to mention a few examples. The computational environmental science community developed a strong need for cloud facilities to store, process, and manage data from observations and numerical models for simulations and forecasts. Weather forecast models and global sensor networks deal with multidimensional geo‐referenced data∖sets. However, environmental data consumer applications usually require a relatively small amount of multidimensional input data slice to analyze a specific area or time interval. Hence, reducing data dimension for information retrieval is mandatory. This paper presents a twofold solution: a technique to load and retrieve the sliced multidimensional data set on different cloud services such as Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. The experimental results performed on these cloud services highlight that the proposed method can significantly speed up the process of loading and retrieving the data slices compared to working with the entire data set in bulk or OPeNDAP server.

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Section: Related Workmentioning

confidence: 99%

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

Mellone,

De Vita,

Sánchez‐Gallegos

et al. 2023

Concurrency and Computation

Self Cite

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“…The IoT data are hierarchically handled through the edge, the fog, the cloud, or any combination of these infrastructures [3]. In the edge [4,5], data are collected by using sensors to measure, for example, environmental data such as the weather [6,7] or health data (e.g., electrocardiogram signals [8]), whereas in the fog [9,10], the data are processed to obtain insights from the data-producing information by using data mining [11] and artificial intelligence [12] applications. Finally, in the cloud, data are stored and processed with big data and data science applications [13] to obtain further information useful in decision-making scenarios [14].…”

Section: Introductionmentioning

confidence: 99%

“…To perform the management and processing of IoT data [16][17][18], multiple applications are deployed on edge-fog-cloud infrastructures, which are organized in the form of processing structures (e.g., pipelines or workflows [19]). In these structures, the applications are managed by using directed acyclic graphs (DAG) [20], where nodes are the applications required for the processing/management of data (e.g., a QRS-complex detector when processing electrocardiogram data [21] or linear regressions when working with weather data for forecast [7]), whereas the edges represent the I/O dependencies between nodes.…”

Section: Introductionmentioning

confidence: 99%

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

Barron

Sánchez‐Gallegos

Carrizales-Espinoza

et al. 2022

Sensors

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Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

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Electricity consumption modeling by a chaotic convolutional radial basis function network

Garcia,

Rubio,

Sossa

et al. 2023

J Supercomput

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A novel transversal processing model to build environmental big data services in the cloud

Cited by 5 publications

References 51 publications

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

Electricity consumption modeling by a chaotic convolutional radial basis function network

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