In the last decade, skyline queries have been extensively studied for different domains because of their wide applications in multi-criteria decision making and search space pruning. A skyline query returns all the interesting points in a multi-dimensional data set that are not dominated by any other point with respect to all dimensions. However, real world data sets are seldom complete, i.e. data points often have missing values in one or more dimensions. Traditional skyline query processing algorithms developed for complete data can not be easily adapted for such situations because of the non-transitive and potentially cyclic nature of dominance relation that arises in the case of incomplete data. Unfortunately, skyline query processing for such incomplete data has not received enough attention. We propose an efficient Sort-based Incomplete Data Skyline (SIDS) algorithm to compute the skyline points over incomplete data. Extensive experiments on both real world and synthetic data sets demonstrate the efficiency and scalability of our approach over current state of the art approach.
Cite as: Bharuka, R. and Kumar. P.S. (2013). Finding Skylines for Incomplete Data. In Proc. Database Technologies 2013 (ADC 2013) Adelaide, Australia. CRPIT, 137. Wang, H. and Zhang, R. Eds., ACS. 109-119
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