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dc.contributor.authorKuicheu, Nathalie Cindyen
dc.contributor.authorWang, N.en
dc.contributor.authorTchuissang, G. N. F.en
dc.contributor.authorXu, Deen
dc.contributor.authorDai, Gujunen
dc.contributor.authorSiewe, Francoisen
dc.date.accessioned2014-09-16T10:42:55Z
dc.date.available2014-09-16T10:42:55Z
dc.date.issued2014-02-27
dc.identifier.citationKuicheu, N.C. et al. (2014) An Iterative Approach to Managing Uncertain Mappings in Dataspace Support Platforms. International Journal of Software Engineering and Knowledge Engineering, 24 (4), pp. 635en
dc.identifier.urihttp://hdl.handle.net/2086/10248
dc.description.abstractA DataSpace Support Platform (DSSP) is a self-sustained and self-managed system which needs to support uncertainty among its mediated schemas and its schema mappings. Some approaches for managing such uncertainty by assigning probabilities and reliability degrees to schema mappings have been proposed. Unfortunately, the number of mappings self-generated by a DSSP is usually too large and among those possible mappings, some might be totally correct and others partially correct. Therefore, providing probabilities or reliability degrees to the mappings is necessary but not su±cient to resolve uncertainty among them. This paper proposes a stepper-based approach called pos-mapping to managing reliable mappings using possibility theory. Instead of choosing a threshold for managing the reliable mappings, pos-mapping approach orders and divides the set of reliable mappings into subsets of possibility distributions and assigns to each of these subsets a recursive possibility degree function. The recursiveness of the possibility degree function leads to an incremental management of the possibility distributions. Experimental results show that our system is more e±cient than the existing systems and the accuracy of the results increases with the number of reliable schemas in the DSSP.en
dc.language.isoenen
dc.publisherWorld Scientific Publishing Companyen
dc.subjectSchema mappingen
dc.subjectreliability degreesen
dc.subjectpossibility theoryen
dc.subjectdataspaceen
dc.titleAn Iterative Approach to Managing Uncertain Mappings in Dataspace Support Platformsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1142/S0218194014500247
dc.researchgroupSoftware Technology Research Laboratory (STRL)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.researchinstituteCyber Technology Institute (CTI)en


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