Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus; Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus; Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus; Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus; Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus; Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus
Wen, Jinfeng1,2; Zhou, Zhangbing1,3; Shi, Zhensheng4; Wang, Junping5,6; Duan, Yucong7; Zhang, Yaqiang1
Source PublicationIEEE ACCESS ; IEEE ACCESS ; IEEE ACCESS ; IEEE ACCESS ; IEEE ACCESS ; IEEE ACCESS
2018 ; 2018 ; 2018 ; 2018 ; 2018 ; 2018
Volume6Pages:40530-40546
SubtypeArticle ; Article ; Article ; Article ; Article ; Article
Abstract

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

;

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

;

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

;

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

;

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

;

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

KeywordScientific Workflow Community Discovery Clustering Abstract Activity Cross-workflow Fragment Recommendation Scientific Workflow Scientific Workflow Scientific Workflow Community Discovery Clustering Scientific Workflow Community Discovery Clustering Community Discovery Clustering Scientific Workflow Abstract Activity Abstract Activity Community Discovery Clustering Abstract Activity Community Discovery Clustering Cross-workflow Fragment Recommendation Cross-workflow Fragment Recommendation Abstract Activity Cross-workflow Fragment Recommendation Abstract Activity Cross-workflow Fragment Recommendation Cross-workflow Fragment Recommendation
WOS HeadingsScience & Technology ; Technology ; Science & Technology ; Science & Technology ; Technology ; Science & Technology ; Technology ; Science & Technology ; Technology ; Technology ; Science & Technology ; Technology
DOI10.1109/ACCESS.2018.2857482 ; 10.1109/ACCESS.2018.2857482 ; 10.1109/ACCESS.2018.2857482 ; 10.1109/ACCESS.2018.2857482 ; 10.1109/ACCESS.2018.2857482 ; 10.1109/ACCESS.2018.2857482
WOS KeywordSERVICE COMPOSITION ; SENSOR-CLOUD ; BIG DATA ; SIMILARITY ; PATTERNS ; SYSTEMS ; SEARCH ; CITY ; SERVICE COMPOSITION ; SERVICE COMPOSITION ; SENSOR-CLOUD ; SERVICE COMPOSITION ; SENSOR-CLOUD ; SERVICE COMPOSITION ; BIG DATA ; BIG DATA ; SENSOR-CLOUD ; SERVICE COMPOSITION ; SIMILARITY ; SIMILARITY ; BIG DATA ; SENSOR-CLOUD ; SENSOR-CLOUD ; PATTERNS ; PATTERNS ; SIMILARITY ; BIG DATA ; BIG DATA ; SYSTEMS ; SYSTEMS ; PATTERNS ; SIMILARITY ; SEARCH ; SIMILARITY ; SEARCH ; SYSTEMS ; PATTERNS ; CITY ; PATTERNS ; CITY ; SEARCH ; SYSTEMS ; SYSTEMS ; CITY ; SEARCH ; SEARCH ; CITY ; CITY
Indexed BySCI ; SCI ; SCI ; SCI ; SCI ; SCI
Language英语 ; 英语 ; 英语 ; 英语 ; 英语 ; 英语
Funding OrganizationNational Natural Science Foundation of China(61772479 ; National Natural Science Foundation of China(61772479 ; National Natural Science Foundation of China(61772479 ; National Natural Science Foundation of China(61772479 ; National Natural Science Foundation of China(61772479 ; National Natural Science Foundation of China(61772479 ; Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications(SKLNST-2018-1-13) ; Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications(SKLNST-2018-1-13) ; Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications(SKLNST-2018-1-13) ; Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications(SKLNST-2018-1-13) ; Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications(SKLNST-2018-1-13) ; Open Foundation of State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications(SKLNST-2018-1-13) ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China ; Fundamental Research Funds for the Central Universities, China University of Geosciences (Beijing), China ; 61662021) ; 61662021) ; 61662021) ; 61662021) ; 61662021) ; 61662021)
WOS Research AreaComputer Science ; Engineering ; Telecommunications ; Computer Science ; Engineering ; Computer Science ; Telecommunications ; Engineering ; Computer Science ; Computer Science ; Computer Science ; Engineering ; Engineering ; Engineering ; Telecommunications ; Telecommunications ; Telecommunications ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications ; Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications ; Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Computer Science, Information Systems ; Computer Science, Information Systems ; Computer Science, Information Systems ; Telecommunications ; Engineering, Electrical & Electronic ; Engineering, Electrical & Electronic ; Engineering, Electrical & Electronic ; Telecommunications ; Telecommunications ; Telecommunications
WOS IDWOS:000441868800018 ; WOS:000441868800018 ; WOS:000441868800018 ; WOS:000441868800018 ; WOS:000441868800018 ; WOS:000441868800018
IS Representative Paper否 ; 否 ; 否 ; 否 ; 否 ; 否
Sub direction classification机器学习 ; 机器学习 ; 机器学习 ; 机器学习 ; 机器学习 ; 机器学习
planning direction of the national heavy laboratory认知机理与类脑学习 ; 认知机理与类脑学习 ; 认知机理与类脑学习 ; 认知机理与类脑学习 ; 认知机理与类脑学习 ; 认知机理与类脑学习
Paper associated data否 ; 否 ; 否 ; 否 ; 否 ; 否
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21827
Collection多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队
Affiliation1.China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China
2.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
3.TELECOM SudParis, Comp Sci Dept, F-91011 Evry, France
4.PetroChina Res Inst Petr Explorat & Dev, Langfang 065007, Peoples R China
5.Chinese Acad Sci, Inst Automat, Lab Precis Sensing, Beijing 100190, Peoples R China
6.Chinese Acad Sci, Inst Automat, Control Ctr, Beijing 100190, Peoples R China
7.Hainan Univ, Coll Informat Sci & Technol, Haikou 570228, Hainan, Peoples R China
Recommended Citation
GB/T 7714
Wen, Jinfeng,Zhou, Zhangbing,Shi, Zhensheng,et al. Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus, Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus, Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus, Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus, Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus, Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus[J]. IEEE ACCESS, IEEE ACCESS, IEEE ACCESS, IEEE ACCESS, IEEE ACCESS, IEEE ACCESS,2018, 2018, 2018, 2018, 2018, 2018,6, 6, 6, 6, 6, 6:40530-40546, 40530-40546, 40530-40546, 40530-40546, 40530-40546, 40530-40546.
APA Wen, Jinfeng,Zhou, Zhangbing,Shi, Zhensheng,Wang, Junping,Duan, Yucong,&Zhang, Yaqiang.(2018).Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus.IEEE ACCESS,6,40530-40546.
MLA Wen, Jinfeng,et al."Crossing Scientific Workflow Fragments Discovery Through Activity Abstraction in Smart Campus".IEEE ACCESS 6(2018):40530-40546.
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