Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure

The scholarly impact of TRECVid (2003-2009)

This paper reports on an investigation into the scholarly impact of the TRECVid (TREC Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis a vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams ‘scored’ highly against the evaluation criteria and the relationship between ‘top scoring’ teams at TRECVid and the ‘top scoring’ papers in terms of citations is analysed. A strong relationship was found between ‘success’ at TRECVid and ‘success’ at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed

Adoção de Cloud Computing nas organizações: uma análise bibliométrica desta tecnologia no contexto da transformação digital

Purpose: The purpose of this study is identify and analyze articles on the adoption of Cloud Computing services in the business field. The study intends to demonstrate the increasing relevance and impact of Cloud Computing technology on business processes.Design/methodology/approach: The study adopted a Bibliometric Review methodology to collect and analyze data. A total of 1,330 articles were collected from the Scopus (Elsevier) database, and various aspects such as authors, journals, and countries were considered. The analysis includes the use of maps to visualize the co-occurrence of terms, co-citation of references, and bibliographic coupling.Findings: The investigation reveals that the adoption of Cloud Computing services in the business environment is a rapidly growing area of research. The study provides an overview of the theme and highlights the significance of Cloud Computing technology in enhancing business processes’ efficiency.Research limitations/implications: The study’s limitations include relying solely on articles available in the Scopus (Elsevier) database and focusing on the period between 2008 and 2020. Future research can expand the analysis by including a broader range of databases and considering a more recent timeframe.Practical implications: The findings of this study have practical implications for businesses, as they highlight the benefits of adopting Cloud Computing services. The technology offers low cost and flexible use, contributing to increased efficiency in business processes.Social implications: The adoption of Cloud Computing services can have significant social impacts by enabling businesses to provide enhanced value to their clients. The technology’s efficiency and flexibility contribute to improved service delivery and customer satisfaction.Originality/value: This study contributes to the advancement of knowledge in the field of Cloud Computing adoption in the business field. The bibliometric analysis provides a comprehensive overview of the research landscape and highlights the key contributions and trends in this area.Finalidade: O objetivo deste estudo é identificar e analisar artigos sobre a adoção de serviços de computação em nuvem na área empresarial. O estudo pretende demonstrar a crescente relevância e impacto da tecnologia Cloud Computing nos processos de negócio. Desenho/metodologia/abordagem: O estudo adotou uma metodologia de Revisão Bibliométrica para coleta e análise de dados. Um total de 1.330 artigos foram coletados da base de dados Scopus (Elsevier), e vários aspectos como autores, periódicos e países foram considerados. A análise inclui o uso de mapas para visualizar a coocorrência de termos, cocitação de referências e acoplamento bibliográfico. Constatações: A investigação revela que a adoção de serviços de computação em nuvem no ambiente de negócios é uma área de pesquisa em rápido crescimento. O estudo oferece uma visão geral sobre o tema e destaca a importância da tecnologia Cloud Computing na melhoria da eficiência dos processos de negócios. Limitações/implicações de pesquisa: As limitações do estudo se apresentam na utilização de somente artigos disponíveis na base de dados Scopus (Elsevier) e em focar no período entre 2008 e 2020. Pesquisas futuras podem expandir a análise incluindo uma gama mais ampla de bases de dados e considerar um período de tempo mais recente. Implicações práticas: Os achados deste estudo têm implicações práticas para as empresas, pois destacam os benefícios da adoção de serviços de computação em nuvem. A tecnologia oferece baixo custo e flexibilidade de uso, contribuindo para o aumento da eficiência nos processos de negócios. Implicações sociais: A adoção de serviços de computação em nuvem pode ter impactos sociais significativos, permitindo que as empresas forneçam maior valor aos seus clientes. A eficiência e a flexibilidade da tecnologia contribuem para melhorar a prestação de serviços e a satisfação do cliente. Originalidade/valor: Este estudo contribui para o avanço do conhecimento na área de adoção de computação em nuvem na área empresarial. A análise bibliométrica fornece uma visão abrangente do cenário de pesquisa e destaca as principais contribuições e tendências nesta área

Human Resource Management and Artificial Intelligence: A Bibliometric Exploration

The concept of artificial intelligence, a driving force behind human resource management, has recently gained popularity in the academic community. This study explores the intellectual structure of this field using the Scopus database in the subject area of business, management and accounting. Bibliographic analysis, a recent and rigorous method for delving into scientific data, is used in this investigation. The approach used is a structured and transparent process divided into four steps: (1) search criteria; (2) selection of database and documents; (3) selection of software and data pre-processing; and (4) analysis of findings. We employ bibliometric mapping to observe their numerous linkages and performance evaluation to learn about their structure. A total of 67 articles were collected from the Scopus database between 2015 and 2022 using certain keywords (artificial intelligence, expert systems, big data analytics, and human resource management) and some specific filters (subject–business, management and accounting; language-English; document–article, review articles and source-journals). Ten research clusters were identified: Cluster 1: multi-agent system; Cluster 2: decision support system; Cluster 3: internet of things; Cluster 4: active learning; Cluster 5: decision tree; Cluster 6: optimisation; Cluster 7: software design; Cluster 8: data mining; Cluster 9: cloud computing; Cluster 10: human-robot interaction. The findings could be helpful for researchers and practitioners in the HRM field to extend their knowledge and understanding of AI and HRM research. This study can provide notable guidance and future directions for quite a few firms in expanding the use of AI in HRM. Keywords: Artificial intelligence, human resource management, bibliometric analysi

Software tools for conducting bibliometric analysis in science: An up-to-date review

Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between universities, the effect of state-owned science funding on national research and development performance and educational efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis and visualization tools. The included tools were divided into three categories: general bibliometric and performance analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and scientometric analysis, they have been developed for a different purpose. The number of exportable records is between 500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny. VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to decide the desired analysis output and chose the option that better fits into their aims

An Approach for Optimizing Resource Allocation and Usage in Cloud Computing Systems by Predicting Traffic Flow

The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation.The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation