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Journal articles on the topic 'Computing Collaborative'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Norman, Donald A. "Collaborative computing: collaboration first, computing second." Communications of the ACM 34, no. 12 (1991): 88–90. http://dx.doi.org/10.1145/125319.125398.

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2

Samsara, Ladiatno. "Tren publikasi collaborative governance sebuah analisis bibliometrik." Berkala Ilmu Perpustakaan dan Informasi 18, no. 2 (2022): 308–25. http://dx.doi.org/10.22146/bip.v18i2.5513.

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Introduction: This study aims to analyze publication trends related to collaborative governance indexed in Scopus database from first year of publication until June 2022. Data Collection Methods: Collaboratove Governance field journal articles published between 1977-2022 were searched from the scopus database using "Collaborative Governance” keyword. Data Analysis: This study uses an analysis of publication trends in collaborative governance, the most productive countries and the most productive organizations, Co-Authorship, Co- and Citation. Results and Discussion: Research

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3

Ciobanu, Radu-Ioan, Catalin Negru, Florin Pop, Ciprian Dobre, Constandinos X. Mavromoustakis, and George Mastorakis. "Drop computing: Ad-hoc dynamic collaborative computing." Future Generation Computer Systems 92 (March 2019): 889–99. http://dx.doi.org/10.1016/j.future.2017.11.044.

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McCray, Alexa, and Jan H. van Bemmel. "Computing and Collaborative Care." Yearbook of Medical Informatics 06, no. 01 (1997): 4–5. http://dx.doi.org/10.1055/s-0038-1637877.

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5

Shen, Weiming, Yinsheng Li, and Anne James. "Collaborative computing and applications." Journal of Network and Computer Applications 35, no. 1 (2012): 1–2. http://dx.doi.org/10.1016/j.jnca.2011.06.012.

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6

Schooler, Eve M. "Conferencing and collaborative computing." Multimedia Systems 4, no. 5 (1996): 210–25. http://dx.doi.org/10.1007/s005300050025.

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Shen, Weiming, Weidong Li, José A. Pino, and Junzhou Luo. "Collaborative Computing and Applications." JUCS - Journal of Universal Computer Science 20, no. (13) (2014): 1708–11. https://doi.org/10.3217/jucs-020-13.

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Israel, Maya, Quentin M. Wherfel, Saadeddine Shehab, Evan A. Ramos, Adam Metzger, and George C. Reese. "Assessing collaborative computing: development of the Collaborative-Computing Observation Instrument (C-COI)." Computer Science Education 26, no. 2-3 (2016): 208–33. http://dx.doi.org/10.1080/08993408.2016.1231784.

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Sharma, Deepika, and Vikas Kumar. "A Framework for Collaborative and Convenient Learning on Cloud Computing Platforms." International Journal of Web-Based Learning and Teaching Technologies 12, no. 2 (2017): 1–20. http://dx.doi.org/10.4018/ijwltt.2017040101.

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The depth of learning resides in collaborative work with more engagement and fun. Technology can enhance collaboration with a higher level of convenience and cloud computing can facilitate this in a cost effective and scalable manner. However, to deploy a successful online learning environment, elementary components of learning pedagogy must be embedded in the technology framework. A cloud computing technology based learning platform built on the proven learning pedagogies will be most successful attempt to facilitate collaborative and convenient learning. Such platform will support the learne

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Souza, Débora, Gabriele Iwashima, Viviane Cunha Farias da Costa, Carlos Eduardo Barbosa, Jano Moreira de Souza, and Geraldo Zimbrão. "Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era." Future Internet 16, no. 12 (2024): 445. http://dx.doi.org/10.3390/fi16120445.

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The majority of the global population now resides in cities, and this trend continues to grow. In this context, the Internet of Things (IoT) is crucial in transforming existing urban areas into Smart Cities. However, IoT architectures mainly focus on machine-to-machine interactions, leaving human involvement aside. The Internet of Everything (IoE) includes human-to-human and human–machine collaboration, but the specifics of these interactions are still under-explored. As urban populations grow and IoT integrates into city infrastructure, efficient, collaborative architectures become crucial. I

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11

Alonso, Ricardo S., Javier Prieto, Óscar García, and Juan M. Corchado. "Collaborative learning via social computing." Frontiers of Information Technology & Electronic Engineering 20, no. 2 (2019): 265–82. http://dx.doi.org/10.1631/fitee.1700840.

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Panjaitan, Erwin S., Rahmat Budiarto, and Abdul Wadud Ismail. "Pervasive Computing for Collaborative Education." Jurnal SIFO Mikroskil 12, no. 2 (2011): 115–22. http://dx.doi.org/10.55601/jsm.v12i2.39.

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Computing is becoming a part of our daily life and so is the Internet. Pervasive computing aims to make our lives simpler by using the tools which will allow us to serve the Internet and enable us to gain information easily. The tools as mentioned earlier are portable devices which allow any users to plug into powerful networks. These devices are no longer restricted to computers alone as understood previously This article is written based on research conducted concerning the advantages and disadvantages of pervasive computing to university students in Malaysia and the problems faced by studen

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13

Wang, Xinheng, Honghao Gao, and Kaizhu Huang. "Artificial Intelligence in Collaborative Computing." Mobile Networks and Applications 26, no. 6 (2021): 2389–91. http://dx.doi.org/10.1007/s11036-021-01829-y.

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14

Clear, Tony, and Diana Kassabova. "A course in collaborative computing." ACM SIGCSE Bulletin 40, no. 1 (2008): 63–67. http://dx.doi.org/10.1145/1352322.1352159.

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15

Yong, Jianming, Weiming Shen, and Anne James. "Collaborative computing technologies and systems." Journal of Systems and Software 86, no. 7 (2013): 1725–26. http://dx.doi.org/10.1016/j.jss.2013.03.058.

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16

Duffy, Jan. "Collaborative computing, groupware and knowledge." Information Management & Computer Security 4, no. 2 (1996): 39–41. http://dx.doi.org/10.1108/09685229610121026.

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17

Bunn, Julian J. "Collaborative computing environments for HEP." Computer Physics Communications 110, no. 1-3 (1998): 51–58. http://dx.doi.org/10.1016/s0010-4655(97)00153-7.

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18

Chen, H., J. Nunamaker, R. Orwig, and O. Titkova. "Information visualization for collaborative computing." Computer 31, no. 8 (1998): 75–82. http://dx.doi.org/10.1109/2.707620.

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19

Gonzalez, Patricia. "Anthropological Research and Collaborative Computing." Social Science Computer Review 14, no. 1 (1996): 21–23. http://dx.doi.org/10.1177/089443939601400106.

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Nunamaker, J. F. "Collaborative computing: the next millennium." Computer 32, no. 9 (1999): 66–71. http://dx.doi.org/10.1109/mc.1999.789753.

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21

Neyem, Andrés, Sergio Ochoa, and José Pino. "Integrating Service-Oriented Mobile Units to Support Collaboration in Ad-hoc Scenarios." JUCS - Journal of Universal Computer Science 14, no. (1) (2008): 88–122. https://doi.org/10.3217/jucs-014-01-0088.

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Advances in wireless communication and mobile computing extend collaboration scenarios. Mobile workers using computing devices are currently able to collaborate in order to carry out productive, educational or social activities. Typically, collaborative applications intended to support mobile workers involve some type of centralized data or services, because they are designed to work on infrastructure supported wireless networks. This centralization constrains the collaboration capabilities in ad-hoc communication cases. This paper introduces the concept of Service-Oriented Mobile Unit (SOMU)

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22

Barhoun, Rabie. "Reliable Collaboration in a Distributed Collaborative Environment: Modeling and Computing." International Review on Computers and Software (IRECOS) 16, no. 1 (2021): 1. http://dx.doi.org/10.15866/irecos.v16i1.20405.

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23

Sun, Yanzan, Xinkun Xie, Fan Wu, Shunqing Zhang, Shugong Xu, and Yating Wu. "Application Loading and Computing Allocation for Collaborative Edge Computing." IEEE Access 9 (2021): 158481–95. http://dx.doi.org/10.1109/access.2021.3128746.

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24

Anwar, Anwar, Muhammad Reza Zulman, Muhammad Arhami, Nahar Nahar, and Safriadi Safriadi. "Development of a Cloud Computing-Based Collaboration Information System." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 1 (2024): 562–66. http://dx.doi.org/10.59934/jaiea.v4i1.692.

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The rapid advancement of information and communication technology has created a demand for efficient and effective information systems to facilitate collaboration among institutions. This study develops a cloud computing-based collaboration information system designed to enhance real-time communication, data management, and collaboration among users. The system comprises key modules, including collaboration management, document management, and integrated collaboration features. Testing involved various scenarios with 10 to 100 active users simultaneously, revealing that the system maintained s

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25

Feng, Xiaodong, Lingzhi Yi, Ning Liu, Xieyi Gao, Weiwei Liu, and Bin Wang. "An Efficient Scheduling Strategy for Collaborative Cloud and Edge Computing in System of Intelligent Buildings." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 5 (2023): 948–58. http://dx.doi.org/10.20965/jaciii.2023.p0948.

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Edge computing is a new computing method, and task scheduling is challenging work. Using edge computing in intelligent buildings for managing smart home devices has gained popularity because it can reduce the delay and network congestion brought by cloud computing. Edge computing has the advantage of fast response speeds, but its computing capacity is limited. To solve this practical problem, a system framework of collaborative cloud and edge computing is constructed for intelligent buildings. First, the communication time, task completion time, and CPU energy consumption are considered compre

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26

Fei, Hongxiao, Xi Zhang, Jun Long, Limin Liu, and Yunbo Wang. "Towards Multi-Satellite Collaborative Computing via Task Scheduling Based on Genetic Algorithm." Aerospace 10, no. 2 (2023): 95. http://dx.doi.org/10.3390/aerospace10020095.

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With satellite systems rapidly developing in multiple satellites, multiple tasks, and high-speed response speed requirements, existing computing techniques face the following challenges: insufficient computing power, limited computing resources, and weaker coordination ability. Meanwhile, most methods have more significant response speed and resource utilization limitations. To solve the above problem, we propose a distributed collaborative computing framework with a genetic algorithm-based task scheduling model (DCCF-GA), which can realize the collaborative computing between multiple satellit

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27

Lee, Hochul, Jaehun Lee, Young Choon Lee, Hyuck Han, and Sooyong Kang. "Mobile collaborative computing on the fly." Pervasive and Mobile Computing 58 (August 2019): 101027. http://dx.doi.org/10.1016/j.pmcj.2019.05.008.

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28

Brown, Judith, Jeff Wilson, Stevenson Gossage, Chris Hack, and Robert Biddle. "Surface Computing and Collaborative Analysis Work." Synthesis Lectures on Human-Centered Informatics 6, no. 4 (2013): 1–168. http://dx.doi.org/10.2200/s00492ed1v01y201303hci019.

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29

Huh, Soon-Young, Q. B. Chung, and Hyung-Min Kim. "Collaborative Model Management In Departmental Computing." INFOR: Information Systems and Operational Research 38, no. 4 (2000): 373–89. http://dx.doi.org/10.1080/03155986.2000.11732419.

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30

Arenas, Alvaro E., Benjamin Aziz, and Gheorghe Cosmin Silaghi. "Reputation management in collaborative computing systems." Security and Communication Networks 3, no. 6 (2009): 546–64. http://dx.doi.org/10.1002/sec.146.

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31

Guan, Albert, Chun-Hung Lin, and Po-Wen Chi. "Secure Collaborative Computing for Linear Regression." Applied Sciences 14, no. 1 (2023): 227. http://dx.doi.org/10.3390/app14010227.

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Machine learning usually requires a large amount of training data to build useful models. We exploit the mathematical structure of linear regression to develop a secure and privacy-preserving method that allows multiple parties to collaboratively compute optimal model parameters without requiring the parties to share their raw data. The new approach also allows for efficient deletion of the data of users who want to leave the group and who wish to have their data deleted. Since the data remain confidential during both the learning and unlearning processes, data owners are more inclined to shar

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32

Liu, Jing, Xuesong Hai, and Keqin Li. "TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts." Future Internet 16, no. 1 (2023): 6. http://dx.doi.org/10.3390/fi16010006.

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Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges i

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33

Chen, Shichao, Qijie Li, Mengchu Zhou, and Abdullah Abusorrah. "Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm." Sensors 21, no. 3 (2021): 779. http://dx.doi.org/10.3390/s21030779.

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In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore

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34

Wang, Chun Xiao, Ying Guo, Jun Wang, and Liang Li. "Research of Industrial Collaborative Manufacturing System on Based on Cloud Computing." Applied Mechanics and Materials 635-637 (September 2014): 1866–70. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1866.

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This paper analyzes the main problems in informatization of china's manufacturing industry, and researches an industrial collaborative manufacturing system for large and medium-sized manufacturing enterprises taking advantage of the benefits of cloud computing such as resource integration, elastic computing, mass data, and service integration. The system includes technical support system, business support system, security system and service portal, which providing design collaboration services, production collaboration services, business collaboration services, office collaboration services fo

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Chen, Ruoyu, Yanfang Fan, Shuang Yuan, and Yanbo Hao. "Vehicle Collaborative Partial Offloading Strategy in Vehicular Edge Computing." Mathematics 12, no. 10 (2024): 1466. http://dx.doi.org/10.3390/math12101466.

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Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs’ processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs’ demanding tasks and MEC servers’ limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further

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36

Zhou, Yian. "A Cross-Regional Cost Collaborative Optimization Model Based on a BIM Lightweight Engine and Mobile Edge Computing." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 07 (2025): 83–96. https://doi.org/10.3991/ijim.v19i07.54977.

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With the rapid development of building information modeling (BIM) technology, its application in construction projects has become widespread, particularly in the collaborative work during the design, construction, and operation phases. However, traditional BIM models, due to their large data volume and complex structure, present significant challenges in data transmission and real-time processing during cross-regional collaboration. Meanwhile, mobile edge computing (MEC), as an emerging computational paradigm, reduces data transmission latency and enhances processing efficiency by deploying co

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Vlachopoulou, Olympia. "Social Computing for Collaborative Learning in Higher Education." Journal of Digital Art & Humanities 3, no. 2 (2022): 24–33. http://dx.doi.org/10.33847/2712-8148.3.2_2.

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This study aims to investigate the use of the specific social computing tool (Wiki) for collaborative learning, and study students’ perceptions in higher education. The participants of the study were a higher education student cohort in Bahrain. The data was collected through questionnaire and interviews. To demonstrate the intricacy of the investigation topic, a mixed methods data collection method was employed. Wiki as a social computing tool significantly enhances student collaboration, according to the preliminary questionnaire results and interviews with selected respondents. As a social

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38

Nan, Yang. "Study on Collaborative Learning System Design Based on Web Platform." Applied Mechanics and Materials 477-478 (December 2013): 1491–94. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.1491.

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The integration of Web technology to build collaborative learning environment, and systems analysis of Web technology to support collaborative learning theory, technical infrastructure, integration of Web and the advantages of collaborative learning is proposed mode of Web technology and collaborative learning environment architecture model. Finally, Google cloud computing applications - GoogleApp Engine and Google Apps interpreted as an instance of the Web and cloud computing technology is how to integrate, as well as collaborative learning environment integrated collaborative learning enviro

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39

Hussein, Al-Bahadili1 Awad Al-Sabbah2 and Muhammad Abu Arqoub1. "MODELING AND ANALYSIS OF CLOUD COLLABORATIVE COMMERCE." International Journal on Cloud Computing: Services and Architecture (IJCCSA), 3, February (2018): 01–22. https://doi.org/10.5281/zenodo.1434569.

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Cloud computing has the potential to be particularly suitable for collaboration commerce (c-commerce) because it generally requires less extensive customization, development, integration, operation, and maintenance than other computing resources. However, upgrading c-commerce IT infrastructure, to run on cloud computing, faces a number of challenges, such as lack of effective design and implementation models. This paper describes and evaluates the performance of a new model of c-commerce that utilizes the evolving cloud computing technologies; as a result of that it is referred to as cloud col

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40

Thimm, Heiko. "Cloud-Based Collaborative Decision Making." International Journal of Decision Support System Technology 4, no. 4 (2012): 39–59. http://dx.doi.org/10.4018/jdsst.2012100103.

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The complexity of many decision problems of today’s globalized world requires new innovative solutions that are built upon proven decision support technology and also recent advancements in the area of information and communication technology (ICT) such as Cloud Computing and Mobile Communication. A combination of the cost-effective Cloud Computing approach with extended group decision support system technology bears several interesting unprecedented opportunities for the development of such solutions. These opportunities include ubiquitous accessibility to decision support software and, thus,

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41

Li, Zhiying, Xiaonan Wu, Hongxun Jiang, and Xun Liang. "Bridging Expertise: Doctor Recommendations for Cross-Disciplinary Collaborations in Online Medical Consultations." Proceedings of the ACM on Human-Computer Interaction 9, no. 2 (2025): 1–32. https://doi.org/10.1145/3710940.

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Online Medical Consultations (OMCs) are increasingly prevalent in Online Healthcare Communities (OHCs), with a considerable focus on doctor recommendations. However, existing research has predominantly centered on recommending individual specialized doctors, neglecting the exploration of more reliable interdisciplinary options for collaborative consultations. This paper introduces an innovative framework for interdisciplinary doctor recommendations for collaborative work in OMC scenarios, comprising two modules: one for calculating expertise and another for collaborative computing. Leveraging

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42

Ortegat, Guillaume, Donatien Grolaux, Etienne Riviere, and Jean Vanderdonckt. "Engineering the Transition of Interactive Collaborative Software from Cloud Computing to Edge Computing." Proceedings of the ACM on Human-Computer Interaction 6, EICS (2022): 1–31. http://dx.doi.org/10.1145/3532210.

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The "Software as a Service" (SaaS) model of cloud computing popularized online multiuser collaborative software. Two famous examples of this class of software are Office 365 from Microsoft and Google Workspace. Cloud technology removes the need to install and update the software on end users' computers and provides the necessary underlying infrastructure for online collaboration. However, to provide a good end-user experience, cloud services require an infrastructure able to scale up to the task and allow low-latency interactions with a variety of users worldwide. This is a limiting factor for

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43

Wu, Xiaoxin, Huan Chen, Yaoda Liu, and Wenwu Zhu. "A Novel Energy Saving Approach through Mobile Collaborative Computing Systems." International Journal of Handheld Computing Research 1, no. 2 (2010): 1–16. http://dx.doi.org/10.4018/jhcr.2010040101.

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Energy saving has been studied widely in both of computing and communication research communities. For handheld devices, energy is becoming a more and more critical issue because lots of applications running on handhelds today are computation or communication intensive and take a long time to finish. Unlike previous work that proposes computing or communication energy solutions alone, this paper proposes a novel energy savings approach through mobile collaborative systems, which jointly consider computing and communication energy cost. In this work, the authors use streaming video as investiga

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44

Xue, Caijun, Hong Nie, Qingying Qiu, and Peien Feng. "A Peer-to-Peer Distributed Collaborative Optimization System." Scientific Programming 12, no. 2 (2004): 121–31. http://dx.doi.org/10.1155/2004/365010.

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It is difficult to solve design optimization problems of complex systems by using a traditional computing method because complex simulation processes usually lead to large-scale computation. Therefore the distributed computing technology based on decomposition-coordination theory has received much attention by design engineers. This paper studies a peer-to-peer collaborative optimization method based on distributed computing technology in order to examine flexible optimization. A new distributed collaborative optimization framework is proposed, and a coordination method is developed and used t

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45

Patel, S. V., and Kamlendu Pandey. "Design of SOA Based Framework for Collaborative Cloud Computing in Wireless Sensor Networks." International Journal of Grid and High Performance Computing 2, no. 3 (2010): 60–73. http://dx.doi.org/10.4018/jghpc.2010070105.

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WSN deployments are growing at a fast rate; however, current WSN architectures and setup do not promote the sharing of data on an inter-WSN basis. Cloud computing has emerged as a promising area to deal with participatory and collaborative data and services, and is envisaged that collaborative cloud computing WSN could be a viable solution for sharing data and services for WSN applications. In this paper, SOA based architecture has been proposed to support collaborating cloud computing in WSN. The architecture consists of layered service stack that has management, information, presentation and

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46

Prof. Krishnakumar L, Prof Krishnakumar L., and Nisha Mariam Varughese. "Intrusion Detection Using Collaborative Network Security Management System in Cloud Computing." Indian Journal of Applied Research 4, no. 3 (2011): 145–47. http://dx.doi.org/10.15373/2249555x/mar2014/42.

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47

Huang, Lin Na, and Chun Li Liu. "Construction of Collaborative Learning Environment Supported by Cloud-Computing." Applied Mechanics and Materials 543-547 (March 2014): 3581–85. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3581.

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In the field of education, cloud computing, as a basic environment and platform for the future network learning, will bring positive effect on construction of the learners' personal learning environment, on construction of school resources, on development of the educational information system. Based on the analysis of the cloud-computing influence on collaborative learning, this paper puts forward the construction strategy of collaborative learning environment supported by cloud-computing and the specific application case, which will provide reference for cloud-computing application in educati

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48

Huang, Lin Na, Feng Hua Liu, and Chun Li Liu. "Design and Research on Collaborative Learning Program Based on Cloud-Services." Advanced Materials Research 756-759 (September 2013): 1199–203. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1199.

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Cloud computing has changed people's work, life and learning while more and more collaborative learning based on cloud services are applied to the field of education and teaching. This paper analyzes the collaborative learning characteristics supported by cloud computing and designs collaborative learning programs of College Computer Basis based on cloud services.

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Moreno, Nathalie, Alejandro Pérez-Vereda, and Antonio Vallecillo. "Managing Reputation in Collaborative Social Computing Applications." Journal of Object Technology 21, no. 3 (2022): 3:1. http://dx.doi.org/10.5381/jot.2022.21.3.a1.

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50

Shen, Haifeng, Zhonghua Yang, and Chengzheng Sun. "Collaborative Web Computing: From Desktops to Webtops." IEEE Distributed Systems Online 8, no. 4 (2007): 3. http://dx.doi.org/10.1109/mdso.2007.19.

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