Domain-Driven Design of Big Data Systems Based on a Reference Architecture

Salma, Cigdem Avci; Teki̇nerdoğan, Bedir; Athanasiadis, Ioannis N.

doi:10.1016/b978-0-12-805467-3.00004-1

Cited by 17 publications

(8 citation statements)

References 3 publications

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“…These models provide farmers with recommendations on the most suitable crops to cultivate. Our methodology for crop analysis in Figure 3 adheres to the standard stages of data analysis [53][54][55][56]. A significant improvement is the inclusion of multiple classifiers, which are fine-tuned and evaluated to identify the most suitable ones for the input data.…”

Section: Methodsmentioning

confidence: 99%

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Crop Prediction Model Using Machine Learning Algorithms

et al. 2023

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Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform modern agriculture by increasing crop yields while minimizing waste. Fifteen different algorithms have been considered to evaluate the most appropriate algorithms to use in agriculture, and a new feature combination scheme-enhanced algorithm is presented. The results show that we can achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99.46% using Naïve Bayes Classifier and Hoeffding Tree algorithms. These results will indicate an increase in production rates and reduce the effective cost for the farms, leading to more resilient infrastructure and sustainable environments. Moreover, the findings we obtained in this study can also help future farmers detect diseases early, increase crop production efficiency, and reduce prices when the world is experiencing food shortages.

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Section: Methodsmentioning

confidence: 99%

“…The design of big data architecture is one of the most complex challenges, considering that it must be flexible and highly scalable [55]. Ref.…”

mentioning

confidence: 99%

Crop Prediction Model Using Machine Learning Algorithms

et al. 2023

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“…A pipeline can operate on the cluster. This procedure is cost-effective because the scaling-out is more affordable than the scaling-up [18]. Since the workflow is composed as a DAG (directed acyclic graph), each job can be executed as a DAG node [19].…”

Section: Workflow As Graphmentioning

confidence: 99%

MLOps approach in the cloud-native data pipeline design

Pölöskei¹

2021

Acta Tech. Jaurinensis

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The data modeling process is challenging and involves hypotheses and trials. In the industry, a workflow has been constructed around data modeling. The offered modernized workflow expects to use of the cloud’s full abilities as cloud-native services. For a flourishing big data project, the organization should have analytics and information-technological know-how. MLOps approach concentrates on the modeling, eliminating the personnel and technology gap in the deployment. In this article, the paradigm will be verified with a case-study in the context of composing a data pipeline in the cloud-native ecosystem. Based on the analysis, the considered strategy is the recommended way for data pipeline design.

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“…Apart from these, the recent emergence of a wide range of overlapping software frameworks in the literature, each one having a different focus, has resulted in the design of ad-hoc and complex architectural big data solutions (Davoudian and Liu, 2020). According to their focus and contribution, these research works can be classified into four categories: (i) empiricallygrounded architectural design (Galster and Avgeriou, 2011;Angelov et al, 2012, Maier et al, 2013Pääkkönen and Pakkala, 2015); (ii) implementation and deployment of big data systems (Schmidt and Möhring, 2013;Zimmermann et al, 2013;Salma et al, 2017); (iii) database management (Doshi et al, 2013;Zhong et al, 2013); and (iv) analytics integration (Westerlund et al, 2014;Sang et al, 2017). It should be noted that the literature is rich on domain-specific big data architectures, developed in order to address particular problems for specific application domains.…”

Section: Big Data Processingmentioning

confidence: 99%

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Bousdekis

Mentzas

2021

Front. Big Data

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Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only the technical implementation of software solutions but the function of the whole organization. In this paper, we bring together Enterprise Integration and Interoperability, Big Data Processing, and Industry 4.0 in order to identify synergies that have the potential to enable the so-called “Fourth Industrial Revolution.” On this basis, we propose an architectural framework for designing and modeling Industry 4.0 solutions for big data-driven manufacturing operations. We demonstrate the applicability of the proposed framework through its instantiation to predictive maintenance, a manufacturing function that increasingly concerns manufacturers due to the high costs, safety issues, and complexity of its application.

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Domain-Driven Design of Big Data Systems Based on a Reference Architecture

Cited by 17 publications

References 3 publications

Crop Prediction Model Using Machine Learning Algorithms

Crop Prediction Model Using Machine Learning Algorithms

MLOps approach in the cloud-native data pipeline design

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

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