A transparent selective undo algorithm for collaborative editing

Cai, Weiwei; He, Fazhi; Lv, Xiao; Cheng, Yuanfang

doi:10.1109/cscwd.2017.8066687

Cited by 2 publications

(2 citation statements)

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“…With the development of computer technologies such as cloud computing and real-time communication, computer-supported collaborative work has become more and more important and has received extensive attention in recent years. CSCW [16][17][18][19][20] has been extensively studied and has been widely used in codesign [3][4][5][6] and editing [7,[18][19][20]. There are some industrial applications available in the network, such as Google Drive and SharedJS, which are used to help people in different locations share information and collaborate with each other.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies have discussed collaborative systems in designing [1,[3][4][5][6], editing [7], caring [8], education [9] etc., some of which focus on knowledge-based engineering frameworks such as collaborative knowledge generation in crowdsourcing [9,10], knowledge reasoning [11], knowledge management [12], and others [13][14][15]. However, even if the CSCW system can share information with others in the workgroup to some extent, there is still a lot of personal knowledge that is generated directly or indirectly that is either too crude to be understood by the computer, or ignored by others and therefore not well recorded.…”

Section: Introductionmentioning

confidence: 99%

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A framework and prototype system in support of workflow collaboration and knowledge mining for manufacturing value chains

Qin

Peng

Zhang

et al. 2023

IET Collab Intel Manufact

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In the field of industrial design and manufacture, computer-supported collaborative work (CSCW) systems have been widely deployed for better teamwork. However, the traditional CSCW systems have a main drawback in effectively processing and utilising knowledge across different industrial workflows. To bridge this gap, we propose a framework for collaboration between members across the manufacturing value chains to increase efficiency and reduce duplication in team cooperation. The framework contains three parts, namely workflow, knowledge mining, and services. Specifically, the workflow part provides a collaborative environment for multiple users. The knowledge mining part, as the core of the framework, extracts in-context knowledge from workflows. The part of services can interact with users with different users in each workflow, including information recommendation they need in the future or information retrieval they want to know from other workflows. Furthermore, we develop a prototype system for supporting multiple value chains collaboration to verify the effectiveness and efficiency of the framework.

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