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Article Citation - WoS: 20Citation - Scopus: 28Career in Cloud Computing: Exploratory Analysis of In-Demand Competency Areas and Skill Sets(Mdpi, 2022) Ozyurt, Ozcan; Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Derawi, MohammadThis study aims to investigate up-to-date career opportunities and in-demand competence areas and skill sets for cloud computing (CC), which plays a crucial role in the rapidly developing teleworking environments with the COVID-19 pandemic. In this paper, we conducted a semantic content analysis on 10,161 CC job postings using semi-automated text-mining and probabilistic topic-modeling procedures to discover the competency areas and skill sets as semantic topics. Our findings revealed 22 competency areas and 46 skills, which reflect the interdisciplinary background of CC jobs. The top five competency areas for CC were identified as "Engineering", "Development", "Security", "Architecture", and "Management". Besides, the top three skills emerged as "Communication Skills", "DevOps Tools", and "Software Development". Considering the findings, a competency-skill map was created that illustrates the correlations between CC competency areas and their related skills. Although there are many studies on CC, the competency areas and skill sets required to deal with cloud computing have not yet been empirically studied. Our findings can contribute to CC candidates and professionals, IT organizations, and academic institutions in understanding, evaluating, and developing the competencies and skills needed in the CC industry.Article Citation - WoS: 9Citation - Scopus: 20Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data From 2003 To 2023 Using Machine Learning(Mdpi, 2023) Gurcan, Fatih; Ayaz, Ahmet; Dalveren, Gonca Gokce Menekse; Derawi, MohammadThe widespread use of business intelligence products, services, and applications piques the interest of researchers in this field. The interest of researchers in business intelligence increases the number of studies significantly. Identifying domain-specific research patterns and trends is thus a significant research problem. This study employs a topic modeling approach to analyze domain-specific articles in order to identify research patterns and trends in the business intelligence field over the last 20 years. As a result, 36 topics were discovered that reflect the field's research landscape and trends. Topics such as "Organizational Capability", "AI Applications", "Data Mining", "Big Data Analytics", and "Visualization" have recently gained popularity. A systematic taxonomic map was also created, revealing the research background and BI perspectives based on the topics. This study may be useful to researchers and practitioners interested in learning about the most recent developments in the field. Topics generated by topic modeling can also be used to identify gaps in current research or potential future research directions.
