An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

AbstractNowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability

Relationship Between Strategic Dexterity, Absorptive Capacity, and Competitive Advantage

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and agreed to complete the AI Analytics Survey Questionnaire using Wu et al.’s survey. The results of the multiple linear regression were significant F(2, 63) = 54.29, p \u3c .001, R2 = .63. In the final model, both predictors were significant: strategic dexterity (t = 2.48, p = .02, ß = .391) and absorptive capacity (t = 2.61, p = .01, ß = .439). A key recommendation is for SME manufacturing executives and managers to understand how to integrate, build, and orchestrate their strategic digital assets when implementing absorptive capacity strategies within their organization. The implications for positive social change include the potential to provide SME manufacturing executives and managers with an understanding of how these technologies can be integrated into the future of data analytics and automation, the support towards a digital economy, and the social effects of artificial intelligence on the underserved and underrepresented groups

Relationship Between Strategic Dexterity, Absorptive Capacity, and Competitive Advantage

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and agreed to complete the AI Analytics Survey Questionnaire using Wu et al.’s survey. The results of the multiple linear regression were significant F(2, 63) = 54.29, p \u3c .001, R2 = .63. In the final model, both predictors were significant: strategic dexterity (t = 2.48, p = .02, ß = .391) and absorptive capacity (t = 2.61, p = .01, ß = .439). A key recommendation is for SME manufacturing executives and managers to understand how to integrate, build, and orchestrate their strategic digital assets when implementing absorptive capacity strategies within their organization. The implications for positive social change include the potential to provide SME manufacturing executives and managers with an understanding of how these technologies can be integrated into the future of data analytics and automation, the support towards a digital economy, and the social effects of artificial intelligence on the underserved and underrepresented groups