The Skyline operator

By S. Börzsönyi, Donald Kossmann, K. Stocker. Proceedings / International Conference on Data Engineering, 2001

2,704 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a9f60496014fc31ade0edbbee6d6479ca0c4de76

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Citations in the corpus

An energy-efficient mobile recommender system (2010, methodology): “Indeed, skyline computing, which retrieves non-dominated data points, has been extensively studied in the database literature [9, 11, 13, 18 ].”
Recommendations in location-based social networks: a survey (2015, methodology): “, select location candidates using semantic data and make a final recommendation using a skyline operator [9] that takes into account both the price and the distance of the candidate locations.”
An optimal and progressive algorithm for skyline queries (2003, methodology): “Borzsonyi et al. [ BKS01 ] propose an SQL syntax for the skyline operator, according to which the above query would be expressed as: [Select *, From Hotels, Skyline of Price min, Distance min], where min indicates that the price and the distance attributes should be minimized.”
Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, methodology): “More specifically, Rx is composed of rules lying in the Pareto frontier [Börzsönyi et al. 2001] (also known as skyline or maximal vector [Godfrey et al.”
Urban computing with taxicabs (2011, methodology): “Then, we find the skyline set from these selected region pairs according to and , using skyline operator [1].”
Efficient Progressive Skyline Computation (2001, methodology): “In [2], Borzsonyi et.”
Skyline with presorting (2003, methodology): “! would not scale well for larger datasets—or smaller buffer pools—than used in [2].”
Designing Fair Ranking Schemes (2017, background): “There is a robust body of prior work on computing preferences over datasets, divided into two major categories: (1) ranking and top-k [27] query processing for cases where the user has a scoring function in mind, and (2) finding representatives such as skyline [18, 28, 29], and its subsets such as r”
Foundations of Preferences in Database Systems (2002, methodology): “Efficient evaluation algorithms have been given in [4] and [22].”
Combining Generative and Discriminative Model Scores for Distant Supervision (2013, methodology): “We use two schemes to obtain a combined ranking from the two model scores: The ﬁrst is a ranking based on non-dominated sorting by successively computing the Pareto-frontier of the 2-dimensional score vectors (Borzsony et al., 2001; Godfrey et al., 2007).”
Shooting Stars in the Sky: An Online Algorithm for Skyline Queries (2002, methodology): “1As observed in [ BKS01 ], the size of the main-memory buer pool impacts the performance of most Skyline algorithms only marginally.”
Finding k-dominant skylines in high dimensional space (2006, background): “Our first approach to compute k-dominant skyline points from an input data set D (over a set of dimensions S) is similar in spirit to the nested-loop approach [2] in that it makes one sequential scan of the data set.”
Spark: top-k keyword query in relational databases (2007, background): “It can be shown that the candidates in the queue form a sky-line [4] and the skyline sweeps across the Cartesian space of the CN as the algorithm progresses, hence the name of the algorithm.”
Preference SQL - Design, Implementation, Experiences (2002, methodology): “Recently, [BKS01] can be considered as a move into this direction.”
LARS: A Location-Aware Recommender System (2012, neutral): “(2) Preference methods such as skylines [22] (and spatial variants [23]) and location-based top-k methods [24] require users to express explicit preference constraints.”
Selecting skyline services for QoS-based web service composition (2010, background): “Given a set of points in a d-dimensional space, a skyline query [4] selects those points that are not dominated by any other poin t.”
Efficient Distributed Skylining for Web Information Systems (2004, background): “In table 1 we can see that our considerations have been correct (also confirmed by experiments in [4]).”