Layered graph data model for data management of dataspace support platform

  • Authors:

  • Dan Yang;Derong Shen;Tiezheng Nie;Ge Yu;Yue Kou

  • Affiliations:

  • School of information Science&Engineering, Northeastern University, Shenyang and School of Software, University of Science and Technology, LiaoNing, Anshan, China;School of information Science&Engineering, Northeastern University, Shenyang, China;School of information Science&Engineering, Northeastern University, Shenyang, China;School of information Science&Engineering, Northeastern University, Shenyang, China;School of information Science&Engineering, Northeastern University, Shenyang, China

  • Venue:
  • WAIM'11 Proceedings of the 12th international conference on Web-age information management
  • Year:
  • 2011

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Abstract

In order to effective management of heterogeneous data sources in dataspace and provide more high quality services, proposing a unified data model to represent all kinds of data in a simple and powerful way is the foundation of DataSpace Support Platform (DSSP). So we propose a novel layered graph Data Model (called lgDM) which includes Entity Data Graph (GD) and Entity Schema Graph (GS) to capture both associations among entities and associations among entity classes. Moreover we also propose an association mining strategy to try to incrementally find associations with less manual effort. We conduct experiments to evaluate the efficiency and effectiveness of our proposed data model.