Current skyline evaluation techniques are mainly to find the outstanding tuples from a large dataset. In this paper, we generalize the concept of skyline query and introduce a novel type of query, the combinatorial skyline query, which is to find the outstanding combinations from all combinations of the given tuples. The past skyline query is a special abundant when used in decision making, mar...

Skyline queries help users make intelligent decisions over complex data, where different and often conflicting criteria are considered. Current skyline computation methods are restricted to centralized query processors, limiting scalability and imposing a single point of failure. In this paper, we address the problem of parallelizing skyline query execution over a large number of machines by le...

Résumé. Les requêtes skyline constituent un outil puissant pour l’analyse de données multidimensionnelles et la décision multicritère. En pratique, le calcul du skyline peut conduire à deux scénarios : soit (i) un nombre important d’objets sont retournés, soit (ii) un nombre réduit d’objets sont retournés, ce qui peut être insuffisant pour la prise de décisions. Dans cet article, nous abordons ...

When the dimensionality of dataset increases slightly, the number of skyline points increases dramatically as it is usually unlikely for a point to perform equally good in all dimensions. When the dimensionality is very high, almost all points are skyline points. Extract interesting skyline points in high dimensional space automatically is therefore necessary. From our experiences, in order to ...

When issuing user-specific queries, users often have a vaguely defined information need. Skyline queries identify the most “interesting” objects for users’ incomplete preferences, which provides users with intuitive query formulation mechanism. However, the applicability of this intuitive query paradigm suffers from a severe drawback. Incomplete preferences on domain values can often lead to im...

The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-theart skyline computation involves complex tree traversals, data-ordering, and conditional branching to minimize the number of point-to-point comparisons. Meanwhile, GPGPU computing offers the potential for parallelizing skyline computation across thousands of cores. However, atte...

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 univo...

Given a set of n points in a d-dimensional space, we seek to compute the skyline, i.e., those points that are not strictly dominated by any other point, using few comparisons between elements. We study the crowdsourcing-inspired setting ([FRPU94]) where comparisons fail with constant probability. In this model, Groz & Milo [GM15] show three bounds on the query complexity for the skyline problem...

We introduce a variant of the classical PAC multi-armed bandit problem. There is an ordered set of n arms A[1], . . . , A[n], each with some stochastic reward drawn from some unknown bounded distribution. The goal is to identify the skyline of the set A, consisting of all arms A[i] such that A[i] has larger expected reward than all lower-numbered arms A[1], . . . , A[i− 1]. We define a natural ...

Along with the application of the sensor network, there will be large amount of dynamic data coming from sensors. How to dig the useful information from such data is significant. Skyline query is aiming to identify the interesting points from a large dataset. The group-based skyline query is to find the outstanding Pareto Optimal groups which cannot be g-dominated by any other groups with the g...