Efficient computation of combinatorial skyline queries

Finding Group-Based Skyline over a Data Stream in the Sensor Network

“…The most related to our problem are [26][27][28][29][30][31][32][33]. Paper [26] returned the top-k composition skyline, however, this paper did not propose the composition skyline formally.…”

“…Paper [26] returned the top-k composition skyline, however, this paper did not propose the composition skyline formally. Im et al, Zhang et al and Chung et al [27][28][29] defined and researched the group skyline query, they calculated the value of the same attribute of k points to form a group, then compared the dominance relation between the groups using the traditional dominance. The calculate functions commonly used in these work were some aggregate functions, such as, SUM, MAX, and MIN.…”

Parallelization of group‐based skyline computation for multi‐core processors

“…Since Börzsönyi et al first introduced the skyline operator in the database filed, many proposals of improved algorithms, output size sensitive algorithms, top‐ k skyline, query optimizations, and variations of skyline query have been investigated. Regarding the concept of group‐based skyline, the most related works are Im et al, Li et al, Chung et al, Liu et al, and Zhang et al In Im et al, Li et al, Chung et al, and Zhang et al, each group is represented by a representative point, whose attribute values are aggregated over the corresponding attribute values of points in the group. The groups are then compared by their representative points as conventional skyline computation.…”

“…While many aggregate functions can be considered in calculating representative points, they focus on 3 distinct functions that are commonly used in database applications, such as S U M , M I N , and M A X . However, similar to the multi‐objective optimization problem where an aggregate function, such as weighted average, is used to combine the multiple criteria to find a single optimal solution, may fail to capture all skyline groups . Moreover, sometimes it is difficult to choose a good or a meaningful aggregate function; thus, the representative point may not be able to fully represent the corresponding group.…”

“…Although skyline computation is particularly useful in multi‐criteria decision‐making applications, it is inadequate to answer queries that need to analyze not only individual points but also their combinations . Specifically, in many real‐world applications, we need to find groups of points that are not dominated by other groups of equal size.…”