Extending the data model for data-centric metagenomics analysis using scientific workflows in CAMERA

被引:0
|
作者
Altintas I. [1 ]
Chen J. [2 ]
Sedova M. [1 ]
Gupta A. [1 ]
Sun S. [2 ]
Lin A.W. [3 ]
Gujral M. [1 ]
Anand M.K. [1 ]
Li W. [3 ]
Grethe J.S. [3 ]
Ellisman M. [3 ,4 ]
机构
[1] San Diego Supercomputer Center, University of California, San Diego, San Diego, CA
[2] California Institute for Telecommunications and Information Technology, University of California, San Diego, San Diego, CA
[3] Center for Research in Biological Systems, University of California, San Diego, San Diego, CA
[4] Neurosciences and Bioengineering, University of California, San Diego, San Diego, CA
来源
Proceedings - 6th IEEE International Conference on e-Science Workshops, e-ScienceW 2010 | 2010年
关键词
D O I
10.1109/eScienceW.2010.18
中图分类号
学科分类号
摘要
Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (CAMERA) is an eScience project to enable the microbial ecology community in managing the challenges of metagenomics analysis. CAMERA supports extensive metadata based data acquisition and access, as well as execution of metagenomics experiments through standard and customized scientific workflows. Users can use a wide range of community analysis tools to select and invoke integrated annotation of genomic datasets. Users can also search and sort information based on selected metadata over the underlying semantic database. We present the semantic data model of CAMERA and its integration with scientific workflow execution information. We also describe how this model is used to interlink related workflows, where outputs of previous workflow executions can be used as inputs by subsequent workflow executions. We demonstrate the effectiveness of our model and approach through scenarios built on currently supported CAMERA workflows and analysis. © 2010 IEEE.
引用
收藏
页码:49 / 56
页数:7
相关论文
共 50 条
  • [21] Pansharpening using data-centric optimization approach
    Devi, Mutum Bidyarani
    Devanathan, R.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (20) : 7784 - 7804
  • [22] Data-Centric and Model-Centric Approaches for Biomedical Question Answering
    Yoon, Wonjin
    Yoo, Jaehyo
    Seo, Sumin
    Sung, Mujeen
    Jeong, Minbyul
    Kim, Gangwoo
    Kang, Jaewoo
    EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION (CLEF 2022), 2022, 13390 : 204 - 216
  • [23] RDF Data-Centric Storage
    Levandoski, Justin J.
    Mokbel, Mohamed F.
    2009 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, VOLS 1 AND 2, 2009, : 911 - 918
  • [24] The Principles of Data-Centric AI
    Jarrahi, Mohammad Hossein
    Memariani, Ali
    Guha, Shion
    COMMUNICATIONS OF THE ACM, 2023, 66 (08) : 84 - 92
  • [25] Unpacking data-centric geotechnics
    Phoon, Kok-Kwang
    Ching, Jianye
    Cao, Zijun
    UNDERGROUND SPACE, 2022, 7 (06) : 967 - 989
  • [26] (Re)Designing Data-Centric Data Centers
    Ranganathan, Parthasarathy
    Chang, Jichuan
    IEEE MICRO, 2012, 32 (01) : 66 - 70
  • [27] Data-centric decision support
    Kulhavy, R
    PROCEEDINGS OF THE 2002 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2002, 1-6 : 3395 - 3400
  • [28] Data-Centric Mobile Crowdsensing
    Jiang, Changkun
    Gao, Lin
    Duan, Lingjie
    Huang, Jianwei
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2018, 17 (06) : 1275 - 1288
  • [29] Cognitive Data-Centric Systems
    Chang, Leland
    PROCEEDINGS OF THE GREAT LAKES SYMPOSIUM ON VLSI 2017 (GLSVLSI' 17), 2017, : 1 - 1
  • [30] A Data-Centric Approach to Synchronization
    Dolby, Julian
    Hammer, Christian
    Marino, Daniel
    Tip, Frank
    Vaziri, Mandana
    Vitek, Jan
    ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS, 2012, 34 (01):