Smart City Data Science: Towards data-driven smart cities with open research issues
علم داده شهر هوشمند: به سوی شهرهای هوشمند مبتنی بر داده با مسائل تحقیقاتی باز-2022
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
Next generation material interfaces for neural engineering
واسط های مواد نسل بعدی برای مهندسی عصبی-2021
Neural implant technology is rapidly progressing, and gaining broad interest in research fields such as electrical engineering, materials science, neurobiology, and data science. As the potential applications of neural devices have increased, new technologies to make neural intervention longer-lasting and less invasive have brought attention to neural interface engineering. This review will focus on recent developments in materials for neural implants, highlighting new technologies in the fields of soft electrodes, mechanical and chemical engineering of interface coatings, and remotely powered devices. In this context, novel implantation strategies, manufacturing methods, and combinatorial device functions will also be discussed.
The convergence of big data and accounting: innovative research opportunities
همگرایی داده های بزرگ و حسابداری: فرصت های تحقیق نوآورانه-2021
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
Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice
علم داده با Vadalog: نمودارهای دانش با یادگیری ماشینی و استدلال در عمل-2021
Following the recent successful examples of large technology companies, many modern enterprises seek to build Knowledge Graphs to provide a unified view of corporate knowledge, and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modeling, and systems for reasoning with domain knowledge. In this paper, we demonstrate how to perform a broad spectrum of data science tasks in a unified Knowledge Graph environment. This includes data wrangling, complex logical and probabilistic reasoning, and machine learning. We base our work on the state-of-the-art Knowledge Graph Management System Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits such as the Jupyter platform. We argue that this is a significant step forward towards practical, holistic data science workflows that combine machine learning and reasoning in data science.
keywords: گراف دانش | علم داده | یادگیری ماشین | استدلال | استدلال احتمالی | Knowledge Graphs | Data science | Machine learning | Reasoning | Probabilistic reasoning
Predicting academic performance with Artificial Intelligence (AI), a new tool for teachers and students
پیش بینی عملکرد تحصیلی با هوش مصنوعی ، ابزاری جدید برای معلمان و دانش آموزان-2020
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
TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers
آموزش: تحقیقات هوش مصنوعی بدون رمزگذاری: هنر مبارزه بدون جنگ: علم داده برای محققان کیفی-2020
In this tutorial, we show how to scrape and collect online data, perform sentiment analysis, social network analysis, tribe finding, and Wikidata cross-checks, all without using a single line of programming code. In a stepby- step example, we use self-collected data to perform several analyses of the glass ceiling. Our tutorial can serve as a standalone introduction to data science for qualitative researchers and business researchers, who have avoided learning to program. It should also be useful for experienced data scientists who want to learn about the tools that will allow them to collect and analyze data more easily and effectively.
Keywords: Twitter | Data scraping | Sentiment analysis | Tribe finding | Wikidata
A bibliometric review of a decade of research: Big data in business research – Setting a research agenda
بررسی کتابشناختی یک دهه تحقیق: داده های بزرگ در تحقیقات تجاری - تنظیم دستور کار تحقیق-2020
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
Data science in the design of public policies: dispelling the obscurity in matching policy demand and data offer
علم داده در طراحی سیاست های عمومی: رفع ابهام در تطبیق تقاضای سیاست و پیشنهاد داده-2020
Data Science (DS) is expected to deliver value for public governance. In a number of studies, strong claims have been made about the potential of big data and data analytics and there are now several cases showing their application in areas such as service delivery and organizational administration. The role of DS in policy-making has, on the contrary, still been explored only marginally, but it is clear that there is the need for greater investigation because of its greater complexity and its distinctive inter-organizational boundaries. In this paper, we have investigated how DS can contribute to the policy deﬁnition process, endorsing a socio-technical perspective. This exploration has addressed the technical elements of DS - data and processes - as well as the social aspects surrounding the actors’ interaction within the deﬁnition process. Three action research cases are presented in the paper, lifting the veil of obscurity from how DS can support policy-making in practice. The ﬁndings highlight the importance of a new role, here deﬁned as that of a translator, who can provide clarity and understanding of policy needs, assess whether data-driven results ﬁt the legislative setting to be addressed, and become the junction point between data scientists and policy-makers. The three cases and their different achievements make it possible to draw attention to the enabling and inhibiting factors in the application of DS.
Keywords: Data science | Policy | Big data | Framing | Knowledge management | Information systems management | Information management | Human resource management | Business management | Strategic management | Risk management | Information science | Business
Predicting and explaining corruption across countries: A machine learning approach
پیش بینی و توضیح فساد در سراسر کشور: رویکرد یادگیری ماشینی-2020
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
The impact of entrepreneurship orientation on project performance: A machine learning approach
تأثیر گرایش کارآفرینی بر عملکرد پروژه: یک رویکرد یادگیری ماشینی-2020
Recent studies in project management have shown the important role of entrepreneurship orientation of the individuals in project performance. Although identifying the role of entrepreneurship orientation as a critical success factor in project performance has been considered as an important issue, it is also important to develop a measurement system for predicting performance based on the degree of an individual’s entrepreneurial orientation. In this study, we use predictive analytics by proposing a machine learning approach to predict individuals’ project performance based on measures of several aspects of entrepreneurial orientation and entrepreneurial attitude of the individuals. We investigated this relationship using a sample of 185 observations and a range of machine learning algorithms including lasso, ridge, support vector machines, neural networks, and random forest. Our results showed that the best method for predicting project performance is lasso. After identifying the best predictive model, we then used the Bayesian Information Criterion and the Akaike Information Criterion to identify the most significant factors. Our results identify all three aspects of entrepreneurial attitude (social self-efficacy, appearance self-efficacy, and comparativeness) and one aspect of entrepreneurial orientation (proactiveness) as the most important factors. This study contributes to the relationship between entrepreneurship skills and project performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.
Keywords: Project performance | Entrepreneurship orientation | Machine learning | Supervised learning | Predictive analytics