Efficient processing of probabilistic group subspace skyline queries in uncertain databases

Due to the pervasive data uncertainty in many real applications, efficient and effective query answering on uncertain data has recently gained much attention from the database community. In this paper, we propose a novel and important query in the context of uncertain databases, namely probabilistic group subspace skyline (PGSS) query, which is useful in applications like sensor data analysis. Specifically, a PGSS query retrieves those uncertain objects that are, with high confidence, not dynamically dominated by other objects, with respect to a group of query points in ad-hoc subspaces. In order to enable fast PGSS query answering, we propose effective pruning methods to reduce the PGSS search space, which are seamlessly integrated into an efficient PGSS query procedure. Furthermore, to achieve low query cost, we provide a cost model, in light of which uncertain data are pre-processed and indexed. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our proposed approaches.

► We define a novel probabilistic group subspace skyline query on uncertain data.
► We design effective subspace/Markov–Chebyshev/dual pruning methods for PGSS.
► We seamlessly integrate pruning methods into an efficient PGSS query procedure.
► We provide a cost model to guide PGSS pre-processing and indexing with low cost.