@article{470,
  author = {Imen Ketata, Riad Mokadem, Franck Morvan},
  title = {Semantic Resource Discovery in Large Scale Environments*},
  journal = {Journal of Digital Information Management},
  year = {2011},
  volume = {9},
  number = {3},
  doi = {},
  url = {http://www.dline.info/jnt/fulltext/v2n2/1.pdf},
  abstract = {In biosciences, data mining is concerned with processing large amount of data which is characterized by heterogeneity, ever changing and spread in different complex environments. Resource discovery from massive data poses a formidable task for many newer as well as routine applications. The issues addressed in the massive data environments so far are the heterogeneity issues and the semantic focus is less. In the current work, we deal with the resource discovery in large-scale environments (as data grid systems) considering data semantic heterogeneity of biomedical sources. There are many benefits such as-(i) allowing a permanent access, through an addressing system, from any domain ontology DOi to another DOj (inter-domain discovery) despite peers’ dynamicity, (ii) reducing the maintenance cost and (iii) taking into account the semantic heterogeneity.},
}