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Cutting-edge technologies, with a special emphasis on the Internet of Things (IoT), tend to operate as game changers, generating enormous alterations in both traditional and modern enterprises. Understanding multiple uses of IoT has become vital for effective financial management, given the ever-changing nature of organizations and the technological disruptions that come with this paradigm change. IoT has proven to be a powerful tool for improving operational efficiency, decision-making processes, overall productivity, and data management. As a result of the continuously expanding data volume, there is an increasing demand for a robust IT system capable of adeptly handling all enterprise processes. Consequently, businesses must develop suitable IoT architectures that can efficiently address these continually evolving requirements. This research adopts an incremental explanatory approach, guided by the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A rigorous examination of 84 research papers has allowed us to delve deeply into the current landscape of IoT research. This research aims to provide a complete and cohesive overview of the existing body of knowledge on IoT. This is accomplished by combining a rigorous empirical approach to categorization with ideas from specialized literature in the IoT sector. This study actively contributes to the ongoing conversation around IoT by recognizing and critically examining current difficulties. This, consequently, opens new research possibilities and promotes future developments in this ever-changing sector. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Social implications: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

Big data is the fuel for powering pervasive artificial intelligence (AI) applications. Aiming at promoting efficient cooperation in data market for big data, in this paper we propose a novel cooperative data purchase framework, by leveraging the power of the data user crowd and their intrinsic trustworthy collaboration relationships. For achieving efficient cooperative data purchase, we develop a comprehensive approach consisting of both data purchase group formation and selection. For the data purchase group formation, we partition the users into multiple data purchase groups for the purpose of budget pooling, by taking into account their data interest and budget levels, meanwhile respecting their underlying collaboration relationships and the maximum allowable group size for data sharing enforced by the data market platform. For the data purchase group selection, we construct a data purchase flow network formulation and devise a minimum cut based solution for selecting the proper set of data purchase groups to fully support their demands subject to the budget constraints. We extensively evaluate the performance of cooperative data purchase framework using both Erdos-Renyi and scale-free collaboration graphs. The numerical results demonstrate that the proposed framework can achieve superior performance, with more than ${40}\%$ and ${100}\%$ performance gain over the case without cooperation in terms of the total received payment and the number of satisfied users, respectively. [ABSTRACT FROM AUTHOR]

Starting with Artificial Intelligence (AI) and continuing with Machine Learning, Blockchain, IoT, Intelligent Learning Platforms, Quantum Computing, Deep Learning, Cognitive Solutions, Learning Management Systems (LMS), Intelligent Global Talent Databases, Prediction Machines and Robotic Process Automation (RPA), talent operators in the whole world are in search of implementing modern technology solutions for enhancing operational efficiency and for developing more engaged, collaborative and better connected organizations. Corporate Japan has seen an unprecedented need for revamping talent operations and organizational structures in order to incorporate modern technology and to enhance global competitiveness. Along with modern technology comes modern talent - a rapidly emerging generation of digitally native business practitioners who redefine the business landscape with their new skills, vision for the future of doing business, desires to connect and aspirations for growth. In an effort to determine how modern technological solutions have changed the dynamics of traditional Japanese organizations and reshaped the architecture of their business, this paper reveals some of the most popular technologies and talent operation practices embraced by Japanese organizations ready to compete on the global market, as illustrated in the case of NTT Communications. [ABSTRACT FROM AUTHOR]

The rapid development of Artificial Intelligence/deep learning technology and its implementation into routine clinical imaging will cause a major transformation to the practice of radiology. Strategic positioning will ensure the successful transition of radiologists into their new roles as augmented clinicians. This paper describes an overall vision on how to achieve a smooth transition through the practice of augmented radiology where radiologists-in-the-loop ensure the safe implementation of Artificial Intelligence systems. [ABSTRACT FROM AUTHOR]

This paper reports the results obtained from use of project complexity parameters in modeling effort estimates. It highlights the attention that complexity has recently received in the project management area. After considering that traditional knowledge has consistently proved to be prone to failure when put into practice on actual projects, the paper endorses the belief that there is a need for more open-minded and novel approaches to project management. With a view to providing some insight into the opportunities that integrate complexity concepts into model building offers, we extend the work previously undertaken on the complexity dimension in project management. We do so analyzing the results obtained with classical linear models and artificial neural networks when complexity is considered as another managerial parameter. For that purpose, we have used the International Software Benchmarking Standards Group data set. The results obtained proved the benefits of integrating the complexity of the projects at hand into the models. They also addressed the need of a complex system, such as artificial neural networks, to capture the fine nuances of the complex systems to be modeled, the projects. [ABSTRACT FROM AUTHOR]

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation. [ABSTRACT FROM PUBLISHER]

Abstract: The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts' and knowledge engineers' and difficulties with the process of acquiring knowledge from domain experts. Within the .field of artificial intelligence, this has been called the 'knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts' and represent it in the appropriate computer format. Given the 'paradox of expertise', the experts' have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others' in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM)field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of... [ABSTRACT FROM AUTHOR]