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Finding Probabilistic Skyline Points by using Dimensionality Reduction and Boundary detection Approach in Distributed Environment
2015
International Journal of Software Engineering and Its Applications
A skyline of a n-dimensional data contains the data objects that are not dominated by any other data object on all dimensions. However, as the number of data dimensions increases the probability of domination points become very low, accordingly the number of points in the skyline becomes large. Also skyline search space has been identified as the key problem in real-time multidimensional databases. None of the traditional search techniques include the use of dimensionality reduction to optimize
doi:10.14257/ijseia.2015.9.8.15
fatcat:63fjzj5z4zgazeov4f2rhgffe4