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This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes.

The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations.

The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future.

The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning.

Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners.

The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving.

The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

This paper aims to study the economic effects of Cloud Computing for a sample of Israeli firms. The authors propose a framework that considers how this technology affects firm performance also introducing the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

The model is estimated through structural equation modeling. The data set consists of the microdata of the survey of information and communication technologies uses and cyber protection in business conducted in Israel by the Central Bureau of Statistics.

The results point to Cloud Computing as a crucial technology to increase firm performance, presenting significant direct and indirect effects as the use of complementary technologies maximizes its impact. Firms that enjoy most direct economic gains from Cloud Computing appear to be the smaller ones, although larger enterprises seem more capable to assimilate complementary technologies, such as Big Data and Machine Learning. The total effects of cloud on firm performance are quite similar among manufacturing and service firms, although the composition of the different effects involved is different.

This paper is one of the very few analyses estimating the impact of Cloud Computing on firm performance based on country microdata and, to the best of the authors’ knowledge, the first one that contemplates the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

The purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.

We have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.

We apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.

This research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.

Findings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.

The findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.

Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.

Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.

This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.

Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.

This paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.

AI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.

There is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.

This paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.

This paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.