Review
Computer Science, Interdisciplinary Applications
Fangyu Li, Yuanjun Laili, Xuqiang Chen, Yihuai Lou, Chen Wang, Hongyan Yang, Xuejin Gao, Honggui Han
Summary: The construction industry is undergoing an intelligent revolution enabled by technologies like IoT, cloud computing, and robotics. Utilizing diverse big data from multiple sources can enhance efficiency, reduce waste and expenses, improve planning and decision-making processes, lower errors, and enhance safety at construction sites. This article provides a comprehensive review of the advantages and current state of big data in the construction industry, addressing unresolved difficulties and offering thoughts on its potential future.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2023)
Review
Computer Science, Information Systems
Anayo Chukwu Ikegwu, Henry Friday Nweke, Chioma Virginia Anikwe, Uzoma Rita Alo, Obikwelu Raphael Okonkwo
Summary: This paper surveys the trends of BDA tools and methods, discusses potential applications and challenges, and provides insightful recommendations.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Mert Onuralp Gokalp, Ebru Gokalp, Kerem Kayabay, Selin Gokalp, Altan Kocyigit, P. Erhan Eren
Summary: Business success today is powered by data-centric software, with big data analytics (BDA) generating valuable insights and empowering strategic decision-making. This study addresses the research gap in the BDA domain by proposing a process capability assessment model with six levels, aiming to improve business value through identifying current capability levels and creating a roadmap for continuous improvement.
COMPUTER STANDARDS & INTERFACES
(2022)
Review
Engineering, Industrial
Devinder Kumar, Rajesh Kr Singh, Ruchi Mishra, Ilias Vlachos
Summary: Supply chain decarbonisation is a strategic requirement in the era of a net-zero economy, yet there is a lack of systematic evaluation of the application of big data analytics (BDA) in this area. This study conducted a systematic literature review and selected 69 papers published between 2016 and 2021 that focused on the application of BDA technology for supply chain decarbonisation. The findings reveal the evolving nature of this topic and the use of resource-advantage theories, organizational theories, and system theories in the studies.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Business
Awad Elsayed Awad Ibrahim, Ahmed A. Elamer, Amr Nazieh Ezat
Summary: This study explores the potential convergence points between big data and accounting and presents exciting research questions for future studies. By reviewing literature and proposing new ideas, the research develops new points of convergence between big data and accounting. The conclusion indicates that big data has the potential to overcome data limitations in accounting techniques and shows significant convergence with three accounting theories.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Computer Science, Artificial Intelligence
Jose Ramon Saura, Domingo Ribeiro-Soriano, Daniel Palacios-Marques
Summary: The social Internet of Things (SIoT) involves sharing data processed by IoT devices for analysis using Big Data techniques, leading to privacy concerns. This study aims to identify perspectives on user privacy in SIoT and promote the concept of privacy by default. Key findings include areas of application for SIoT and challenges faced by the industry.
Review
Engineering, Electrical & Electronic
Doygun Demiroll, Resul Das, Davut Hanbay
Summary: This study focuses on the security and privacy issues of big data, examining relevant literature and current technologies, and presenting an analysis of security requirements and the elimination of vulnerabilities against cyber attacks.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Business
Jie Sheng, Joseph Amankwah-Amoah, Zaheer Khan, Xiaojun Wang
Summary: The article reviews the methodological innovations in studying big data analytics and discusses how they can be better utilized to examine contemporary organizational issues, especially 'black swan' events like the COVID-19 global crisis.
BRITISH JOURNAL OF MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Katarzyna Biesialska, Xavier Franch, Victor Muntes-Mulero
Summary: The study aims to link Agile software development with Big Data analytics and found that data-driven software development is focused on areas such as code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics. It concludes that improving the productivity of software development teams is a key objective faced by Big Data analytics in the industry and provides scholars with insights into the state and trends of software analytics research in the business environment.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Review
Management
Ramin Raeesi, Navid Sahebjamnia, S. Afshin Mansouri
Summary: Container Terminals (CTs) are facing complex planning tasks and environmental pressures. This paper reviews the developments in this field and identifies research opportunities for better exploitation of environmental considerations and data utilization.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Review
Automation & Control Systems
Uichin Lee, Gyuwon Jung, Eun-Yeol Ma, Jin San Kim, Heepyung Kim, Jumabek Alikhanov, Youngtae Noh, Heeyoung Kim
Summary: With the rise of digital therapeutics, the development of software as a medical device for mobile and wearable devices has become increasingly important. Current evaluations of digital therapeutics primarily focus on effectiveness, but to gain a deeper understanding of engagement and adherence, analysis of contextual and interaction data from these devices is necessary. This review of data-driven analytics provides researchers and practitioners with guidance on exploring and analyzing digital therapeutic datasets, examining contextual patterns, and establishing the relationship between engagement and adherence.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Computer Science, Theory & Methods
Lars Lundberg
Summary: This paper presents a program and methodology for bibliometric mining of research trends and directions. The method is applied to the research area of Big Data from 2012 to 2022 using the Scopus database. The top 10 research directions in Big Data are identified, as well as the role of Big Data research in various fields. Analysis is also conducted on the activity levels of different geographic regions and the citation scores of documents from different regions and research directions.
JOURNAL OF BIG DATA
(2023)
Article
Computer Science, Interdisciplinary Applications
Haosheng Huang, Xiaobai Angela Yao, Jukka M. Krisp, Bin Jiang
Summary: The article discusses the increasing prevalence of location/activity sensing technologies and location-based services, leading to a large volume and variety of location-based big data (LocBigData) and the opportunities and pitfalls it presents for research on urban systems and human environments. It also explores the role of LocBigData in realizing smart cities, summarizing research trends and challenges in the field.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Review
Business
Yucheng Zhang, Shan Xu, Long Zhang, Mengxi Yang
Summary: The lack of sufficient big data-based approaches hinders the advancement of HRM research and practices. Scholars are aware of the importance of applying big data approaches, but there is a need for clear guidance on integration. This study provides a framework for conducting big data research in HRM and proposes a future research agenda and challenges in the era of big data.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Computer Science, Information Systems
Iris Figalist, Christoph Elsner, Jan Bosch, Helena Holmstrom Olsson
Summary: This study investigates how to enable continuous monitoring of information needs and the generation of knowledge and insights for stakeholders involved in the lifecycle of software-intensive products. Results indicate that specifying information needs and sharing and reusing knowledge, tools, and concepts can accelerate the generation of knowledge and insights, benefiting stakeholders early on in the analysis process.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Automation & Control Systems
Tajudeen Olawale Olasupo, Carlos E. Otero
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2020)
Article
Physics, Multidisciplinary
Trevor Herntier, Koffi Eddy Ihou, Anthony Smith, Anand Rangarajan, Adrian Peter
Article
Economics
Luz C. Ortega, Luis Daniel Otero, Mitchell Solomon, Carlos E. Otero, Aldo Fabregas
Summary: Low visibility conditions have negative impacts on safety and traffic operations, leading to serious accidents. Due to the complexity and variability of weather variables, visibility forecasting is a challenging task for transportation agencies. This study explores the application of deep learning models using time series climatological data for single-step visibility forecasting. Five different deep learning models were developed, trained, and tested using data from weather stations in Florida, which is one of the states heavily affected by low visibility problems. The authors discuss the results of the models and suggest future research directions.
INTERNATIONAL JOURNAL OF FORECASTING
(2023)
Article
Physics, Multidisciplinary
Trevor Herntier, Adrian M. Peter
Summary: This research focuses on finding the closest multivariate Gaussian distribution on a constraint surface to a given distribution using the techniques of the calculus of variations. The study also examines the intermediate distributions along the geodesics to understand the evolution of uncertainty.
Proceedings Paper
Computer Science, Information Systems
Xavier Merino, Carlos E. Otero
Summary: Containerization technology allows applications to operate in dynamic environments and be time-aware. Although time namespace is not supported by container engines, it is possible to create time-aware containers through a specific workflow, and the performance overhead of time virtualization in containers has been analyzed.
2022 IEEE CLOUD SUMMIT
(2022)
Proceedings Paper
Computer Science, Information Systems
David Nieves-Acaron, Benjamin Luchterhand, Akshay Aravamudan, David Elliott, Steven Wyatt, Carlos E. Otero, Luis D. Otero, Anthony O. Smith, Adrian M. Peter, Wesley Jones, Eric Lam
Summary: The work introduces a new plugin called ACE for battlefield Situational Awareness, utilizing edge acoustic classification technology to improve audio intelligence collection and analysis, enhancing warfighters' SA capabilities.
2021 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM 2021)
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Xavier Merino, Carlos Otero, David Nieves-Acaron, Benjamin Luchterhand
Summary: The growth of the Internet-of-Things has led to increased demand for computing, storage, and network resources, surpassing the capabilities of the cloud model. Fog computing has emerged as a solution to optimize resource utilization and reduce latency, by decentralizing resources and enabling efficient data communication and processing closer to the end-user.
Proceedings Paper
Computer Science, Information Systems
Juan C. Avendano, Luis Daniel Otero, Carlos Otero
Summary: This paper introduces an optimal sensor placement technique for Structural Health Monitoring systems, which utilizes load values and filtering algorithms to identify the best sensor placement positions in a steel bridge model.
2021 15TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON 2021)
(2021)
Proceedings Paper
Computer Science, Information Systems
Juan C. Avendano, Luis Daniel Otero, Carlos Otero
Summary: This paper presents the design and development of a structural health monitoring system tailored for transportation infrastructure components, focusing on the application of statistical machine learning algorithms to classify deformation datasets of a bridge. The study utilized computer simulation and gradient boosting neural networks to predict the behavior of transportation infrastructures and evaluate the performance of ML techniques.
2021 15TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON 2021)
(2021)
Proceedings Paper
Computer Science, Information Systems
Luz C. Ortega, Luis Daniel Otero, Carlos E. Otero, Aldo Fabregas
2020 14TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON2020)
(2020)
Proceedings Paper
Computer Science, Theory & Methods
David Elliott, Evan Martino, Carlos E. Otero, Anthony Smith, Adrian M. Peter, Benjamin Luchterhand, Eric Lam, Steven Leung
2020 IEEE 6TH WORLD FORUM ON INTERNET OF THINGS (WF-IOT)
(2020)
Article
Computer Science, Artificial Intelligence
Mark Moyou, Anand Rangarajan, John Corring, Adrian M. Peter
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Information Systems
Luz C. Ortega, Luis Daniel Otero, Carlos Otero
Article
Computer Science, Information Systems
Yunus Egi, Carlos E. Otero
Proceedings Paper
Computer Science, Theory & Methods
Xavier Merino, Carlos Otero, Matthew Ridley, David Elliott
PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD)
(2018)
