Abstract
Uncertainty is inherent in many important applications, such as data integration, environmental surveillance, location-based services (LBS), sensor monitoring and radio-frequency identification (RFID). In recent years, we have witnessed significant research efforts devoted to producing probabilistic database management systems, and many important queries are re-investigated in the context of uncertain data models. In the paper, we study the problem of top k dominating query on multi-dimensional uncertain objects, which is an essential method in the multi-criteria decision analysis when an explicit scoring function is not available. Particularly, we formally introduce the top k dominating model based on the state-of-the-art top k semantic over uncertain data. We also propose effective and efficient algorithms to identify the top k dominating objects. Novel pruning techniques are proposed by utilizing the spatial indexing and statistic information, which significantly improve the performance of the algorithms in terms of CPU and I/O costs. Comprehensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of our techniques.
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Zhan, L., Zhang, Y., Zhang, W., Lin, X. (2014). Identifying Top k Dominating Objects over Uncertain Data. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8421. Springer, Cham. https://doi.org/10.1007/978-3-319-05810-8_26
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DOI: https://doi.org/10.1007/978-3-319-05810-8_26
Publisher Name: Springer, Cham
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