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The skyline query, aiming at identifying a set of skyline tuples that are not dominated by any other tuple, is particularly useful for multicriteria data analysis and decision making. For uncertain databases, a probabilistic skyline query, called P-Skyline, has been developed to return skyline tuples by specifying a probability threshold. However, the answer obtained via a P-Skyline query usually includes skyline tuples undesirably dominating each other when a small threshold is specified; or it may contain much fewer skyline tuples if a larger threshold is employed. To address this concern, we propose a new uncertain skyline query, called U-Skyline query, in this paper. Instead of setting a probabilistic threshold to qualify each skyline tuple independently, the U-Skyline query searches for a set of tuples that has the highest probability (aggregated from all possible scenarios) as the skyline answer. In order to answer U-Skyline queries efficiently, we propose a number of optimization techniques for query processing, including 1) computational simplification of U-Skyline probability, 2) pruning of unqualified candidate skylines and early termination of query processing, 3) reduction of the input data set, and 4) partition and conquest of the reduced data set. We perform a comprehensive performance evaluation on our algorithm and an alternative approach that formulates the U-Skyline processing problem by integer programming. Experimental results demonstrate that our algorithm is 10-100 times faster than using CPLEX, a parallel integer programming solver, to answer the U-Skyline query. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of sufficient set and necessary set for distributed processing of probabilistic top-k queries in cluster-based wireless sensor networks. These two concepts have very nice properties that can facilitate localized data pruning in clusters. Accordingly, we develop a suite of algorithms, namely, sufficient set-based (SSB), necessary set-based (NSB), and boundary-based (BB), for intercluster query processing with bounded rounds of communications. Moreover, in responding to dynamic changes of data distribution in the network, we develop an adaptive algorithm that dynamically switches among the three proposed algorithms to minimize the transmission cost. We show the applicability of sufficient set and necessary set to wireless sensor networks with both two-tier hierarchical and tree-structured network topologies. Experimental results show that the proposed algorithms reduce data transmissions significantly and incur only small constant rounds of data communications. The experimental results also demonstrate the superiority of the adaptive algorithm, which achieves a near-optimal performance under various conditions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two types of probabilistic aggregation queries, namely, Probabilistic Minimum Value Queries (PMVQ)s and Probabilistic Minimum Node Queries (PMNQ)s. A PMVQ determines possible minimum values among all imprecise sensed data, while a PMNQ identifies sensor nodes that possibly provide minimum values. However, centralized approaches incur a lot of energy from battery-powered sensor nodes and well-studied in-network aggregation techniques that presume precise sensed data are not practical to inherently imprecise sensed data. Thus, to answer PMVQs and PMNQs energy-efficiently, we devised suites of in-network algorithms. For PMVQs, our in-network minimum value screening algorithm (MVS) filters candidate minimum values; and our in-network minimum value aggregation algorithm (MVA) conducts in-network probability calculation. PMNQs requires possible minimum values to be determined a prior, inevitably consuming more energy to evaluate than PMVQs. Accordingly, our one-phase and two-phase in-network algorithms are devised. We also extend the algorithms to answer PMNQ variants. We evaluate all our proposed approaches through cost analysis and simulations. [ABSTRACT FROM PUBLISHER]