9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

Supporting Adaptable Granularity of Changes for Massive-scale Collaborative Editing

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2013.254123,
        author={Luc Andre and Stephane Martin and Gerald Oster and Claudia-Lavinia Ignat},
        title={Supporting Adaptable Granularity of Changes for Massive-scale Collaborative Editing},
        proceedings={9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={ICST},
        proceedings_a={COLLABORATECOM},
        year={2013},
        month={11},
        keywords={collaborative editing consistency maintenance optimistic replication computer-supported collaborative work},
        doi={10.4108/icst.collaboratecom.2013.254123}
    }
    
  • Luc Andre
    Stephane Martin
    Gerald Oster
    Claudia-Lavinia Ignat
    Year: 2013
    Supporting Adaptable Granularity of Changes for Massive-scale Collaborative Editing
    COLLABORATECOM
    IEEE
    DOI: 10.4108/icst.collaboratecom.2013.254123
Luc Andre1,*, Stephane Martin1, Gerald Oster1, Claudia-Lavinia Ignat2
  • 1: Universite de Lorraine, France
  • 2: Inria, France
*Contact email: luc.andre@loria.fr

Abstract

Since the Web 2.0 era, the Internet is a huge content editing place in which users contribute to the content they browse. Users do not just edit the content but they collaborate on this content. Such shared content can be edited by thousands of people. However, current consistency maintenance algorithms seem not to be adapted to massive collaborative updating. Shared data is usually fragmented into smaller atomic elements that can only be added or removed. Coarse-grained data leads to the possibility of conflicting updates while fine-grained data requires more metadata. In this paper we offer a solution for handling an adaptable granularity for shared data that overcomes the limitations of fixed-grained data approaches. Our approach defines data at a coarse granularity when it is created and refines its granularity only for facing possible conflicting updates on this data. We exhibit three implementations of our algorithm and compare their performances with other algorithms in various scenarios.