Big Data Management for Machine Learning from Big Data

Olawoyin, Anifat M.; Leung, Carson K.; Hryhoruk, Connor C. J.; Cuzzocrea, Alfredo

doi:10.1007/978-3-031-29056-5_35

Cited by 10 publications

(4 citation statements)

References 46 publications

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“…Eliminating UX-compliant demands can lead to UX disregard in the end result as well as poor resource planning [15]. Automation of UX evaluation is limited by a lack of datasets and suitable evaluation models [16].…”

Section: Introductionmentioning

confidence: 99%

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Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network

Xu Yang

2024

jes

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The study of user perception and interaction with applications is referred to as user experience, or UX. The intricacy and versatility of software products, from requirements engineering to product functionality are well recognized. UX evaluations are often depends on prototypes, but it's important to consider the semantics embedded in software requirements to ensure project success. In this manuscript, Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network (SRE-UEDM-BDA-CBTCN) is proposed. The input data are collected from Requirements dataset. The collected data are given to the Convolution-Bidirectional Temporal Convolutional Network (CBTCN) to Design Modeling of Big Data Analysis user experience based on the dataset. In general, CBTCN does not express any adaption of optimization techniques for determining the ideal parameters to accurate Design user experience. Hence, African Vultures Optimization Algorithm (AVOA) is proposed in this work to improve the weight parameter of CBTCN. The proposed model is implemented and the efficiency is evaluated utilizing some performance metrics like accuracy, precision, specificity, sensitivity and F1-Score. The proposed SRE-UEDM-BDA-CBTCN method provides 28.46%, 21.34 and 33.81% higher accuracy, 22.88%, 26.52% and 34.63% higher Precision and 28.46%, 21.34 and 33.81% higher specificity compared with the existing techniques like Holistic big data integrated artificial intelligent modeling to improve privacy and safety in data management of smart cities (AIM-BDI-SDM), Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach (MHA-UED-MLA) and Towards Measuring User Experience based on Software Requirements (TM-UEB-SR).

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Section: Introductionmentioning

confidence: 99%

“…ratio of accurately predicted positive (true positives) among the actual positive instances is sensitivity. It is assesses the capacity to detect positive instances correctly which is computed by eqn(16).…”

mentioning

confidence: 99%

Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network

Xu Yang

2024

jes

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“…Data Velocity : In order to derive useful insights from data streams in real-time as they are being created, high-performance computing skills are required (Bharany, 2023 ; Olawoyin et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.

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“…Nowadays, developing technology and the rise in the volume of information make data analysis a complex process. The evolving challenges of handling the volume and complexity of Big Data have led to several studies focusing on machine-learning models [1]. Weather forecasting is based on large datasets derived from radars, satellites, and groundbased measurements, thus requiring efficient data storage, processing, and data mining technologies [2].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Rainfall Nowcasting Using Numerical Model and Observational Data

Kyros,

Manolas,

Diamantaras

et al. 2023

Comecap 2023

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Big Data Management for Machine Learning from Big Data

Cited by 10 publications

References 46 publications

Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network

Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

A Machine Learning Approach for Rainfall Nowcasting Using Numerical Model and Observational Data

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