Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’ is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting useful knowledge or actionable insights from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the scientific study and analysis of actual happenings with historical data using a variety of scientific methodologies, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ‘‘Smart City Data Science’’, where city data collected from various sources such as sensors, Internet-connected devices, or other external sources, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, artificial intelligence, particularly, machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world city services. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, industry professionals, as well as policy-makers of a country, particularly, from the technological point of view.
keywords: شهرهای هوشمند | علم داده | فراگیری ماشین | اینترنت اشیا | تصمیم گیری داده محور | خدمات هوشمند | امنیت سایبری | Smartcities | Datascience | Machinelearning | InternetofThings | Data-drivendecisionmaking | Intelligentservices | Cybersecurity

This study aims to develop accounting standards, curriculums, and research to cope with the rapid development of big data. The study presents several potential convergence points between big data and different accounting techniques and theories. The study discusses how big data can overcome the data limitations of six accounting issues: financial reporting, performance measurement, audit evidence, risk management, corporate budgeting and activity-based techniques. It presents six exciting research questions for future research. Then, the study explains the potential convergence between big data and agency theory, stakeholders theory, and legitimacy theory. This theoretical study develops new convergence points between big data and accounting by reviewing the literature and proposing new ideas and research questions. The conclusion indicates a significant conver- gence between big data and accounting on the premise that data is the heart of accounting. Big data and advanced analytics have the potential to overcome the data limitations of accounting techniques that require estimations and predictions. A remarkable convergence is argued between big data and three accounting the- ories. Overall, the study presents helpful insights to members of the accounting and auditing community on the potential of big data.
keywords: اطلاعات بزرگ | تجزیه و تحلیل | حسابداری | علم داده | هوش تجاری | Big data | Analytics | Accounting | Data science | Business intelligence

Abstract—Learning Analytics (LA) is data science applied to the educational field. It enables the measurement, collection, and analysis of learners’ data and their context. In this research we utilized two algorithms from the field of artificial intelligence (AI): KNearest Neighbor and Random Forest. These algorithms trained a predictive model for the academic performance of students pursuing an engineering degree. This research found that a general picture of the performance of the group is enough to improve, despite the forecast for each student not being accurate. This allowed the instructor to adapt their teaching technique to get better results. Finally, most students agree to take advantage of LA and they think that knowing their predictive results at the beginning of the course will help them do better in class.
Keywords: Artificial intelligence | Educational innovation | Learning analytics | Higher education

The last several years have witnessed a surge of interest in artificial intelligence (AI). As the foundation of AI technologies, big data has attracted attention of researchers. Big data and data science have been recognized as new tools and methodologies for developing theories in business research (George, 2014). While several qual- itative reviews have been conducted, there is still a lack of a quantitative and systematic review of big data in business research. Our review study fills this gap by depicting the development of big data in business research using bibliometric methods, such as publication counts and trends analysis, co-citation analysis, co-authorship analysis and keywords co-occurrence analysis. Based on the sample of 1366 primary focal articles and 55,718 secondary references, we visualize the landscape and evolution of big-data business research and capture the developmental trajectory and trends over time (between 2008 and 2018). Furthermore, based on our analyses, we provide several promising directions for future research. In doing so, we provide scholars with a systematic understanding of the development and panoramic roadmap of big data research in business.
Keywords: Big Data | Management and business | Bibliometric review | Scientific visualization | CiteSpace

In the era of Big Data, Analytics, and Data Science, corruption is still ubiquitous and is perceived as one of the major challenges of modern societies. A large body of academic studies has attempted to identify and explain the potential causes and consequences of corruption, at varying levels of granularity, mostly through theoretical lenses by using correlations and regression-based statistical analyses. The present study approaches the phenomenon from the predictive analytics perspective by employing contemporary machine learning techniques to discover the most important corruption perception predictors based on enriched/enhanced nonlinear models with a high level of predictive accuracy. Specifically, within the multiclass classification modeling setting that is employed herein, the Random Forest (an ensemble-type machine learning algorithm) is found to be the most accurate prediction/classification model, followed by Support Vector Machines and Artificial Neural Networks. From the practical standpoint, the enhanced predictive power of machine learning algorithms coupled with a multi-source database revealed the most relevant corruption-related information, contributing to the related body of knowledge, generating actionable insights for administrator, scholars, citizens, and politicians. The variable importance results indicated that government integrity, property rights, judicial effectiveness, and education index are the most influential factors in defining the corruption level of significance
Keywords: Corruption perception | Machine learning | Predictive modeling | Random forest | Society policies and regulations |Government integrity | Social development