Metric spaces in data mining: applications to clustering
SIGSPATIAL Special
Efficient probabilistic reverse nearest neighbor query processing on uncertain data
Proceedings of the VLDB Endowment
Continuous inverse ranking queries in uncertain streams
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Continuous probabilistic count queries in wireless sensor networks
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Efficient processing of probabilistic set-containment queries on uncertain set-valued data
Information Sciences: an International Journal
MUD: Mapping-based query processing for high-dimensional uncertain data
Information Sciences: an International Journal
Efficient fuzzy ranking queries in uncertain databases
Applied Intelligence
Probabilistic ranking in fuzzy object databases
Proceedings of the 21st ACM international conference on Information and knowledge management
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This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that is assumed to be mutually exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ranking position, the probability of the object falling at that ranking position. The resulting rank probability distribution can serve as input for several state-of-the-art probabilistic ranking models. Existing approaches compute this probability distribution by applying the Poisson binomial recurrence technique of quadratic complexity. In this paper, we theoretically as well as experimentally show that our framework reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object. Furthermore, we show how the output of our method can be used to apply probabilistic top-k ranking for the objects, according to different state-of-the-art definitions. We conduct an experimental evaluation on synthetic and real data, which demonstrates the efficiency of our approach.