Getting the Best from Uncertain Data

The skyline of a relation is the set of tuples that are not dominated by any other tuple in the same relation, where tuple u dominates tuple v if u is no worse than v on all the attributes of interest and strictly better on at least one attribute. Previous attempts to extend skyline queries to probabilistic databases have proposed either a weaker form of domination, which is unsuitable to univocally define the skyline, or a definition that implies algorithms with exponential complexity. In this paper we demonstrate how, given a semantics for linearly ranking probabilistic tuples, the skyline of a probabilistic relation can be univocally defined. Our approach preserves the three fundamental properties of skyline: 1) it equals the union of all top-1 results of monotone scoring functions, 2) it requires no additional parameter to be specified, and 3) it is insensitive to actual attribute scales. We also detail efficient sequential and index-based algorithms

Getting the Best from Uncertain Data: the Correlated Case ⋆ Getting the Best from Uncertain Data: the Correlated Case Extended Abstract

Abstract. In this extended abstract we apply the notion of skyline to the case of probabilistic relations including correlation among tuples. In particular, we consider the relevant case of the x-relation model, consisting of a set of generation rules specifying the mutual exclusion of tuples. We show how our definitions apply to different ranking semantics and analyze the time complexity for the resolution of skyline queries.

Getting the Best from Uncertain Data: the Correlated Case

In this extended abstract we apply the notion of skyline to the case of probabilistic relations including correlation among tuples. In particular, we consider the relevant case of the x-relation model, consisting of a set of generation rules specifying the mutual exclusion of tuples. We show how our definitions apply to different ranking semantics and analyze the time complexity for the resolution of skyline queries

Getting the Best from Uncertain Data: the Correlated Case

In this extended abstract we apply the notion of skyline to the case of probabilistic relations including correlation among tuples. In particular, we consider the relevant case of the x-relation model, consisting of a set of generation rules specifying the mutual exclusion of tuples. We show how our definitions apply to different ranking semantics and analyze the time complexity for the resolution of skyline queries