Big Data Analytics with Machine Learning Tools

Fowdur, Tulsi Pawan; Beeharry, Yogesh; Hurbungs, V.; Bassoo, Vandana; Ramnarain-Seetohul, Vidasha

doi:10.1007/978-3-319-60435-0_3

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Big Data means very large data sets, with both structured and unstructured data, that come at high speed from different sources (Fowdur et. al., 2018).…”

Section: Big Datamentioning

confidence: 99%

Theoretical Approach to Big Data Analytics and Neuromarketing: Advances in Understanding Consumer Trends

Kuş¹,

Šević²

2021

Digital Siege

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The daily life of individuals is on a path of digital transformation. This process creates a paradigm shift in consumer trends analysis and consumer behaviour. Consequently, a positivist and digitally supported perspective in the process of the analysis of consumer trends has emerged. Competences like intuition and foresight have begun to be supported with data analysis. This is because digital technologies cause consumers to produce data through devices and platforms. Moreover , techniques such as neuromarketing, which integrates neuroscience with marketing, create new opportunities for consumer trend analysis. This paper aims to shed light on the concept of digital transformation, and on the ways in which reliable data and scientifically-supported methods such as big data and neuromarketing contribute to the analysis of consumer trends. In this sense, it also contributes to the use of digital measurement in a marketing context and to the development of a positivist marketing perspective. .

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“…Big Data means very large data sets, with both structured and unstructured data, that come at high speed from different sources (Fowdur et. al., 2018).…”

Section: Big Datamentioning

confidence: 99%

Theoretical Approach to Big Data Analytics and Neuromarketing: Advances in Understanding Consumer Trends

Kuş¹,

Šević²

2021

Digital Siege

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“…Hence, the mixture of land, labour, and capital, or known as the "means of production", in the production process has changed leading scholars, journalists and entrepreneurs to talk about the 4th Industrial Revolution and Internet of Things (IoT). Computer specs are growing constantly making possible the connection of physical objects/devices to the Internet with the ability to identify themselves to other devices via wireless technologies, sensor technologies, or QR codes resulting in massive datasets or Big Data (Fowdur et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Big Data as a reform opportunity for public sector and real economy: The case of Greece

Lotsis,

Georgousis,

Papakostas

2024

F1000Res

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Background The recent economic recession has hit Greece with economic, political, and social repercussions. Structural reforms in the real economy and public sector are considered to be of paramount importance for introducing a new consumption and production paradigm to achieve sustainable economic growth. Methods The present paper aims to highlight Big Data and the Internet of Things as part of the 4th Industrial Revolution as a potential enabler for the necessary leap in the 21st century for the Greek public sector on the theoretical basis of the Unified Growth Theory and the Washington Doctrine. In the present paper, an evaluation of the Greek public sector is attempted by using two different indices, the Digital Maturity Index of the Hellenic Federation of Enterprises (SEV), and the Digital Economy and Society Index (DESI) of the EU. Results Findings strongly indicate that the introduction of digital skills in the educational system along with vocational training of older groups regarding the use of digital public services is an important factor for digital services implementation in the case of Greece. Moreover, the findings also underlie that demand for digital public services is not merely a matter of economic power. Other causes that lie in cultural, geographical, and behavioural habits should also be considered. Conclusions Greece is not in the pole position regarding Big Data implementation. High-speed broadband, both fast and ultrafast, lacks widespread availability, while prices remain relatively high compared to other European countries. Internet user skills and advanced IT skills remain mostly misused in the private and the public sector, while business digitization, e-commerce, e-Government, and e-health remain relatively low.

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“…Data management, machine learning, and artificial intelligence have been emerging themes in the energy sector during recent years, thanks to the increasing availability of data and the decreasing cost of sensors, storage, and data manipulation. Data analytics methods have already been used to analyze collected data to improve energy efficiency, for example in buildings [1,2], or combined with machine learning methods [3]. Different machine learning methods have been already defined [4], such as clustering, k-nearest neighbors (kNN), regression models, principal component analysis (PCA), artificial neural networks (ANNs), and support vector machines (SVMs).…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods are implemented in different environments: MATLAB [16][17][18] and R [19,20] are the most famous ones for research. Recently, Fowdur et al have provided an overview of the available platforms (mainly commercial, such as IBM solution, Hewlett-Packard Enterprise Big Data Platform, SAP HANA Platform, Microsoft Azure and Oracle Big Data, but also open source software, such as H2O) on machine learning, and how it could be used for big data analytics [3]. Commercial environments have a more robust tool already developed and test with better documentation than open source software such as R. These environments would be preferred if the research aim is to develop commercial software.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator

Vialetto

Noro

2019

Energies

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In recent years, collecting data is becoming easier and cheaper thanks to many improvements in information technology (IT). The connection of sensors to the internet is becoming cheaper and easier (for example, the internet of things, IOT), the cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under development and/or being introduced to create values using data. In this paper, a clustering approach for the short-term forecasting of energy demand in industrial facilities is presented. A model based on clustering and k-nearest neighbors (kNN) is proposed to analyze and forecast data, and the novelties on model parameters definition to improve its accuracy are presented. The model is then applied to an industrial facility (wood industry) with contemporaneous demand of electricity and heat. An analysis of the parameters and the results of the model is performed, showing a forecast of electricity demand with an error of 3%.

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Big Data Analytics with Machine Learning Tools

Cited by 8 publications

References 11 publications

Theoretical Approach to Big Data Analytics and Neuromarketing: Advances in Understanding Consumer Trends

Theoretical Approach to Big Data Analytics and Neuromarketing: Advances in Understanding Consumer Trends

Big Data as a reform opportunity for public sector and real economy: The case of Greece

Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator

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