Article
Computer Science, Hardware & Architecture
Yueshen Xu, Xinyu Zhao, Zhiping Jiang, Zhibo Qiu, Lei Hei, Rui Li
Summary: The rapid development of IoT computing has led to many problems, particularly in the management of massive mobile services. It is crucial to assign proper semantic annotations to these services in order for developers to find suitable services and providers to generate revenue. Existing approaches lack the utilization of the natural association between services, providers, and users. In this study, we propose a new model called GoT, which constructs a HIN for service data and fully leverages structural and semantic information to improve recommendation accuracy and alleviate the cold-start problem.
MOBILE NETWORKS & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Paulo Sergio Santos Junior, Monalessa Perini Barcellos, Ricardo de Almeida Falbo, Joao Paulo A. Almeida
Summary: The article discusses the challenges of integrating data from different applications used in supporting the Scrum process, proposing the development of Scrum Reference Ontology (SRO) to address these semantic issues. The successful application of SRO in a Brazilian government agency's software development unit demonstrates its potential to serve as an interlingua for application integration in the context of Scrum support, facilitating the development of integrated data-driven solutions for decision making.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Computer Science, Theory & Methods
Maroua Masmoudi, Sana Ben Abdallah Ben Lamine, Hajer Baazaoui Zghal, Bernard Archimede, Mohamed Hedi Karray
Summary: This study introduces a knowledge hypergraph-based approach for data integration and querying, applied to Earth Observation data. Results show that the proposed method enhances query processing in terms of accuracy, completeness, and semantic richness of response.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Jianyong Shi, Zeyu Pan, Liu Jiang, Xiaohui Zhai
Summary: With the development of digital city and smart city construction, the integration of BIM, GIS, and IoT has received much attention. However, the traditional integration mainly focuses on data conversion, and there is a lack of unified data structure expression. This study aims to establish a general CIM ontology to integrate heterogeneous BIM, GIS, and IoT data. Based on the CIM ontology, an application ontology is built to illustrate rule-based mapping, querying, and inferring.
ADVANCED ENGINEERING INFORMATICS
(2023)
Review
Environmental Sciences
Hansi Zhang, Hui Hu, Matthew Diller, William R. Hogan, Mattia Prosperi, Yi Guo, Jiang Bian
Summary: An individual's health is influenced by a complex interplay between genetics and environmental exposures. While genetics only account for about 10% of health conditions, the remaining is determined by environmental factors. Comprehensive understanding of disease causes and prevention requires systematic exploration of environmental exposures, especially the external exposome. However, the heterogeneity of external exposome data sources increases the difficulty of analyzing and understanding the associations between environmental exposures and health outcomes. Development of semantic standards using ontologies is crucial to provide a consistent understanding of variables and relationships in heterogeneous data sources.
ENVIRONMENTAL RESEARCH
(2021)
Article
Computer Science, Software Engineering
Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir
Summary: The article discusses the development of biomedical data integration systems, focusing on the features and advantages of the IPDS system, as well as its application cases. By introducing the development and application of the IPDS system, it demonstrates the potential of the system in biomedical data integration.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Information Systems
Rana Alaa, Mariam Gawish, Manuel Fernandez-Veiga
Summary: The semantic web is an extension of the current web, providing information with well-defined meanings to facilitate global cooperation and knowledge exchange. Ontologies serve as the backbone of the semantic web, offering a common foundation for resources. Its use of semantics and artificial intelligence results in a Smarter Web, allowing for easier product retrieval and enhancing e-commerce platforms.
Review
Engineering, Industrial
Junliang Wang, Chuqiao Xu, Jie Zhang, Ray Zhong
Summary: This paper provides a comprehensive review of big data analytics (BDA) for intelligent manufacturing systems, covering the concepts, methodologies, and applications. BDA has shown great potential in improving the efficiency and outcomes of product design, manufacturing, and maintenance. However, there are still challenges and opportunities that need further research and exploration.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Roberto Reda, Filippo Piccinini, Giovanni Martinelli, Antonella Carbonaro
Summary: The paper discusses the use of data from wearable fitness devices and health appliances to improve clinical decision making, as well as the current obstacles and opportunities in this area. An approach using Web Semantic technologies and Linked Open Data is proposed to address the integration of heterogeneous health data, with a web portal developed for integrating, sharing, and analyzing health and fitness data.
Article
Computer Science, Information Systems
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram
Summary: The paper introduces JointDNN, an engine for collaborative computation between mobile devices and the cloud for DNNs, which improves energy and performance efficiency for mobile devices while reducing the workload and communications for the cloud server. By processing some layers on mobile devices and others on the cloud server, JointDNN can adapt to battery limitations, server load constraints, and quality of service requirements, achieving significant reductions in latency and mobile energy consumption compared to traditional approaches.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Biology
Luke T. Slater, John A. Williams, Paul N. Schofield, Sophie Russell, Samantha C. Pendleton, Andreas Karwath, Hilary Fanning, Simon Ball, Robert Hoehndorf, Georgios V. Gkoutos
Summary: Annotation of biomedical entities with ontology classes facilitates semantic analysis and utilization of background knowledge. We have developed a new tool called Klarigi, which introduces multiple scoring heuristics to identify compositional and discriminatory classes for annotated entity groups. Klarigi utilizes semantic inference, classification, and significance testing to provide characteristic and explanatory explanations for biomedical datasets, offering a distinct perspective compared to traditional enrichment methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Information Systems
Dagimawi D. Eneyew, Miriam A. M. Capretz, Girma T. Bitsuamlak
Summary: This paper introduces the concept and significance of smart-building digital twins in the field of architecture. It proposes a novel multi-layer digital twin architecture to address interoperability issues in smart buildings, along with an ontology-based query mediation method. Through experimental evaluation, the feasibility of the architecture and method is demonstrated.
Review
Chemistry, Physical
Simon Clark, Francesca L. Bleken, Simon Stier, Eibar Flores, Casper Welzel Andersen, Marek Marcinek, Anna Szczesna-Chrzan, Miran Gaberscek, M. Rosa Palacin, Martin Uhrin, Jesper Friis
Summary: Battery research and production generate diverse data in various fields, driving modern battery development through a combination of traditional natural sciences and emerging technologies. The use of a battery ontology can unify battery-related activities, accelerate knowledge transfer, and facilitate the integration of artificial intelligence in battery development. A logically consistent and expansive ontology is crucial for supporting battery digitalization and standardization efforts.
ADVANCED ENERGY MATERIALS
(2022)
Article
Computer Science, Artificial Intelligence
Non Sanprasit, Katechan Jampachaisri, Taravichet Titijaroonroj, Kraisak Kesorn
Summary: The study introduces a knowledge-based model and framework that can automatically generate star schemas, addressing the challenges in data warehouse construction. By predicting attribute names and data types, it achieves the automated generation of star schemas, outperforming baseline methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Huifeng Wu, Yi Yan, Baiping Chen, Feng Hou, Danfeng Sun
Summary: This article introduces an ontology and a three-layer cloud-fog-edge architecture (FADA) for large and complex machines to address issues such as data quality and data extraction. Experimental results prove the feasibility and performance advantages of FADA in different scenarios.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Information Systems
Li Da Xu, Lian Duan
ENTERPRISE INFORMATION SYSTEMS
(2019)
Article
Computer Science, Information Systems
Boming Huang, Yuxiang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou
ENTERPRISE INFORMATION SYSTEMS
(2019)
Article
Automation & Control Systems
Wattana Viriyasitavat, Li Da Xu, Zhuming Bi
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2019)
Article
Computer Science, Information Systems
Chengxi Huang, Li Da Xu, Hongming Cai, Guoqiang Li, Jiawei Du, Lihong Jiang
ENTERPRISE INFORMATION SYSTEMS
(2019)
Article
Computer Science, Information Systems
Wenan Tan, Lu Zhao, Lida Xu, Li Huang, Na Xie
ENTERPRISE INFORMATION SYSTEMS
(2020)
Article
Computer Science, Information Systems
Wenan Tan, Yao Zhao, Xiaoming Hu, Lida Xu, Anqiong Tang, Tong Wang
ENTERPRISE INFORMATION SYSTEMS
(2019)
Article
Computer Science, Information Systems
Chen Xia Jin, Fa Chao Li, Kai Zhang, Li Da Xu, Yong Chen
ENTERPRISE INFORMATION SYSTEMS
(2020)
Article
Computer Science, Information Systems
Wenan Tan, Na Xie, Lu Zhao, Lida Xu, Yong Sun
ENTERPRISE INFORMATION SYSTEMS
(2020)
Article
Computer Science, Information Systems
Michael Kataev, Larisa Bulysheva, Lida Xu, Yuri Ekhlakov, Natalia Permyakova, Vukica Jovanovic
ENTERPRISE INFORMATION SYSTEMS
(2020)
Article
Automation & Control Systems
Wentao Wang, Nan Niu, Mounifah Alenazi, Juha Savolainen, Zhendong Niu, Jing-Ru C. Cheng, Li Da Xu
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Computer Science, Artificial Intelligence
Shanshan Zhao, Shancang Li, Lianyong Qi, Li Da Xu
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2020)
Article
Computer Science, Cybernetics
Wattana Viriyasitavat, Li Da Xu, Zhuming Bi
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2020)
Article
Management
Li Da Xu
SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE
(2020)
Article
Computer Science, Cybernetics
Li Da Xu, Wattana Viriyasitavat
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2019)
Article
Computer Science, Cybernetics
Yongping Zhang, Xiwei Xu, Ang Liu, Qinghua Lu, Lida Xu, Fei Tao
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2019)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)