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Resilient distributed processing technique (RDPT), in which mapper and reducer are simplified with the Spark contexts and support distributed parallel query processing.

The proposed work is implemented with Pig Latin with Spark contexts to develop query processing in a distributed environment.

Query processing in Hadoop influences the distributed processing with the MapReduce model. MapReduce caters to the works on different nodes with the implementation of complex mappers and reducers. Its results are valid for some extent size of the data.

Pig supports the required parallel processing framework with the following constructs during the processing of queries: FOREACH; FLATTEN; COGROUP.

The purpose of this paper is to make an overview of 6,618 publications of Apache Hadoop from 2008 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field, as well as a preliminary knowledge of Apache Hadoop for interested researchers.

This paper employs the bibliometric analysis and visual analysis approaches to systematically study and analyze publications about Apache Hadoop in the Web of Science database. This study aims to investigate the topic of Apache Hadoop by means of bibliometric analysis with the aid of visualization applications. Through the bibliometric analysis of the collected documents, this paper analyzes the main statistical characteristics and cooperation networks. Research themes, research hotspots and future development trends are also investigated through the keyword analysis.

The research on Apache Hadoop is still the top priority in the future, and how to improve the performance of Apache Hadoop in the era of big data is one of the research hotspots.

This paper makes a comprehensive analysis of Apache Hadoop with methods of bibliometrics, and it is valuable for researchers can quickly grasp the hot topics in this area.

This paper draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years, aiming to understand the development status and trends in this field and inspire new ideas for researchers.

The purpose of this paper to ensure the rapid developments in the radio frequency wireless technology, the synthesis of frequencies for pervasive wireless applications is crucial by implementing the design of low voltage and low power Fractional-N phase locked loop (PLL) for controlling medical devices to monitor remotely patients.

The developments urge a technique reliable to phase noise in designing fractional-N PLL with a new eight transistor phase frequency detector and a good linearized charge pump (CP) for speed of operation with minimum mismatches.

In applications for portable wireless devices, by proposing a new phase-frequency detector with the removal of dead, blind zones and a modified CP to minimize the mismatch of currents.

The results are simulated in 45 nm complementary metal oxide semiconductor generic process design kit (GPDK) technology in cadence virtuoso. The phase noise of the proposed Fractiona-N phase locked loop has–93.18, –101.4 and –117 dBc/Hz at 10 kHz, 100 kHz and 1 MHz frequency offsets, respectively, and consumes 3.3 mW from a 0.45 V supply.

Through bibliometric analysis, the study intends to reveal the evolution of the trends in the Scopus database, the scope of research and the connection between technology and entrepreneurship.

This study uses a comprehensive science mapping approach, integrating network visualisation to map research groups, bibliometric analysis to measure publication trends and thematic analysis to identify overarching themes. This study uses a thorough technique to examine the complex interaction between technology and entrepreneurship from 2000 to 2023. The collection includes information from various sources, creating a corpus of 2,207 documents. These sources include 698 scholarly journals, books and other publications.

According to the report, the interest in technology and entrepreneurship is expanding. The three nations conducting the most study on this subject is the USA, the UK and Italy. Some of the top writers in this area include James A. Cunningham, Alison N. Link and David B. Audretsch.

The study found possibilities and problems associated with the interaction between technology and entrepreneurship. Additionally, the study found several research holes in this area. The study also noted some research gaps in this field, including those related to the sustainability of society and the environment, the effects of entrepreneurship on inequality and the difficulties faced by entrepreneurs in underdeveloped nations.

This study thoroughly overviews the business and technology sectors. It outlines some of the difficulties that must be overcome whilst identifying the main research trends in this field. Researchers, decision-makers and businesspeople interested in using technology for entrepreneurial endeavours can all benefit from the study’s findings.

This study’s dataset’s scope, which might not include all pertinent publications, is one of its limitations. Nevertheless, the results add to a thorough picture of the state of the profession and recent developments. This study’s insights are valuable for researchers, policymakers and entrepreneurs interested in leveraging technology for entrepreneurial pursuits.

The research points to a number of directions that need more inquiry, such as in-depth studies into the social and environmental implications of technology-driven entrepreneurship and methods to combat inequality.

The corona virus outbreak has affected the entrepreneurial ecosystem adversely. This in particular has impacted on females. This study aims to identify the factors affecting the entrepreneurial intention (EI) of females under post-COVID adverse conditions.

A total of 340 articles were screened applying the preferred reporting items for systematic reviews and meta-analyses method. The EI of individuals undertaking different professional courses from three top National Institute Ranking Framework (NIRF) government institutes were then analyzed. In the expert analysis, the nominal group technique (NGT) and analytic hierarchy process (AHP) were used to identify and rank the crucial factors. Subsequently, in the exploratory analysis, a 19-item questionnaire was framed. The data was analyzed using SmartPLS 3.

Resilience, entrepreneurial education, self-concept and self-efficacy, social influence and opportunity perception were identified as critical indicators. Resilience was identified as the most significant factor. The partial least square structural equation modeling (PLS-SEM) revealed that all the factors except social influence had significant effect on the EI of females.

This study focuses primarily on factors affecting females in India. As ecosystems and support vary by region and country, the authors suggest that this study be replicated in different regions/countries in the future.

The potential entrepreneurs can use this study's reference to identify the abilities they need. The government and academic institutions can have skill/training programs to enhance the effect of important factors identified in the study.

While there is growing research of entrepreneurship and entrepreneurial marketing post-pandemic, there are a lack of Indian studies and female entrepreneurship studies.

The purpose of this study is to analyze the entrepreneurial intentions (EI) of women to better understand the cognition process of women which will help to identify the reason for their inadequate participation in the field of entrepreneurship.

This study follows the method of a traditional literature survey followed by scientometric analysis to identify the primary factors to the EI of women. The factors obtained from the literature review were provided to the nominal group, and the process of nominal group technique (NGT) was carried out to frame a research framework. The factors were then ranked based on their significance using the analytic hierarchy process (AHP), taking into consideration the review of experts.

Among the primary factors, personal factors were found to be the most influential factors and weighted 54%. Self-efficacy was found to be the most prominent secondary variable and had a global weight of 22.14%.

The research outcome has implications for policymaking bodies, investors, academicians and potential women entrepreneurs. Future researchers can apply exploratory research methods and confirmatory research methods to test the research framework.

Very few researchers have used the technique of scientometric analysis for the review of literature on EI of women, and to the best of the authors’ knowledge, no earlier researcher has attempted to use the technique of NGT or AHP after scientometric analysis.

The purpose of this article is to (1) map the most important topics in the domain of sustainable business practices for entrepreneurial firms in emerging countries, (2) identify important terms in the various dimensions of sustainability in business and (3) identify the important practices and prioritize the practices.

This study has adopted a unique methodology that combines state-of-art scientometric analysis with the fuzzy nominal group technique (NGT) and fuzzy decision-making trial and evaluation laboratory (DEMATEL). Results obtained from the co-occurrence analysis in scientometrics were further mapped through NGT to obtain the list of the most important topics in the domain. The factors affecting sustainable business practices obtained through topic mapping were analyzed through fuzzy DEMATEL to obtain the cause-and-effect relation of variables.

The scale of firms, leadership, uncertainty, gender, country/location, education and tourism were found to be the factors affecting the sustainable business practices of entrepreneurial firms. The sustainable business practices for entrepreneurial firms were (1) innovation, (2) resilience, (3) policy, (4) business ethics and virtue ethics, (5) business model, (6) upcycling and value creation, (7) collaboration and (8) triple bottom line.

Policymakers in entrepreneurial firms, as well as other organizations, can implement the identified sustainable business practices to obtain optimum results and smooth functioning of the companies. The research framework obtained can be tested using exploratory methods.

Very few researchers have used the technique of scientometric analysis to identify the sustainable business practices of entrepreneurial firms, and to the best of the knowledge of the authors, no earlier researcher has attempted to use the technique of topic mapping, fuzzy NGT and fuzzy DEMATEL in combination.

The purpose of this study offers a theoretical model, hypotheses and empirical analyses of how formal and informal institutions influence the ease of market entry of startups in the context of India.

A theoretical model based upon institutional and market entry theories is presented with hypotheses. Structural equation modeling is used to test the hypotheses.

Results of the study suggest that college preparations and a culture of creativity and innovation are positively related to ease of market entry by Indian startups. A culture of personal success is negatively related to ease of market entry. Government assistance related to ease of market entry is not significant.

This paper offers a new perspective, linking formal and informal institutional influences to startup ease of market entry. In addition, informal institutions are viewed from the cultures of personal success and creativity/innovation within the entrepreneurial domain, which is also new to the literature. This paper offers specific insights in the context of India startups and offers some interesting findings that can contribution to the literature, policy and practice.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

The study aims to identify the essential competencies for Industry 4.0 within the manufacturing sector, to prioritise developing them among the workforce in creating a competitive advantage for the organization.

The study employs the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) methodology to prioritize competencies. Literature review and expert input guided the identification of competencies, which were ranked by experts for their relevance, through pairwise comparisons.

Seven competency groups, encompassing 21 sub-groups, were identified as essential for the Industry 4.0 workforce. Digital-technical and industry-specific competencies emerged as the most prominent to be developed on priority, followed by cognitive and business competencies. Despite their smaller representation, core/generic competencies remain the foundation for developing the newer and more specialised competencies.

Recognising the need for empirical studies in early-adopting organisations of Industry 4.0, future research should explore competencies across industries as well as talent development mechanisms, for a nuanced understanding of competency requirements.

The study informs organisations, educators and policymakers guiding workforce training, talent management and development, educational curriculum aligned with the demands of Industry 4.0 to bridge the competency gaps. It can support India’s strategic initiatives like “Make in India” by fostering a digitally ready and competent workforce.

This research provides an empirically validated, structured framework for Industry 4.0 competency prioritization specific to the manufacturing sector in India. It integrates expert inputs with AHP to rank competencies, offering a contextual understanding of competency requirements. It also contributes to human capital theory by advancing competency mapping for Industry 4.0.