Personalized top-k skyline queries in high-dimensional space

Lee, Jongwuk; You, Gae-won; Hwang, Seung-won

doi:10.1016/j.is.2008.04.004

Cited by 77 publications

(53 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other methods have been proposed that evaluate preference queries in a manner different to skyline and top-k, aiming to enhance the quality of the answer. Examples of these methods include, but are not limited to, hybrid multi-objective methods , k-dominance [Chan et al 2006a], k-frequency [Chan et al 2006b], ranked skylines [Lee et al 2009], k-representative dominance [Lin et al 2007], distance-based dominance [Tao et al 2009], ǫ-skylines [Xia et al 2008], and top-k dominance [Yiu and Mamoulis 2007]. In this paper, we do not propose a new preference method.…”

Section: Preference Methodsmentioning

confidence: 99%

“…c 2013 ACM 0362-5915/2013/-ART0 $15.00 DOI:http://dx.doi.org/10.1145/0000000.0000000 swers by evaluating their personal wishes according to a certain preference method. In the literature, there exist a large number of preference evaluation methods, including top-k [Chaudhuri and Gravano 1999], skylines [Börzsönyi et al 2001], hybrid multi-object methods , k-dominance [Chan et al 2006a], kfrequency [Chan et al 2006b], ranked skylines [Lee et al 2009], k-representative dominance [Lin et al 2007], distance-based dominance [Tao et al 2009], ǫ-skylines [Xia et al 2008], and top-k dominance [Yiu and Mamoulis 2007]. In general, the point of proposing new preference methods is to challenge the notion of "best" answers.…”

Section: Introductionmentioning

confidence: 99%

“…This approach treats the DBMS as a "black box", where the preference evaluation method is completely decoupled from the database, and hence not concerned with internal database operations (e.g., joins) necessary to retrieve the data (e.g., see Chan et al 2006a;2006b;Lee et al 2009;Lin et al 2007;Tao et al 2009;Xia et al 2008;Yiu and Mamoulis 2007]). The main advantage of this approach is its simplicity as it only requires the implementation of the preference evaluation method in a separate code base outside the core database engine.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Flexible and extensible preference evaluation in database systems

Levandoski

Eldawy

Mokbel

et al. 2013

TODS

View full text Add to dashboard Cite

Personalized database systems give users answers tailored to their personal preferences. While numerous preference evaluation methods for databases have been proposed (e.g., skyline, top-k, k-dominance, kfrequency), the implementation of these methods at the core of a database system is a double-edged sword. Core implementation provides efficient query processing for arbitrary database queries, however this approach is not practical since each existing (and future) preference method requires implementation within the database engine. To solve this problem, this paper introduces FlexPref, a framework for extensible preference evaluation in database systems. FlexPref, implemented in the query processor, aims to support a wide-array of preference evaluation methods in a single extensible code base. Integration with FlexPref is simple, involving the registration of only three functions that capture the essence of the preference method. Once integrated, the preference method "lives" at the core of the database, enabling the efficient execution of preference queries involving common database operations. This paper also provides a query optimization framework for FlexPref, as well as a theoretical framework that defines the properties a preference method must exhibit to be implemented in FlexPref. To demonstrate the extensibility of FlexPref, this paper also provide case studies detailing the implementation of seven state-of-the-art preference evaluation methods within FlexPref. We also experimentally study the strengths and weaknesses of an implementation of FlexPref in PostgreSQL over a range of single -table and multi-table preference queries.

show abstract

Section: Preference Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flexible and extensible preference evaluation in database systems

Levandoski

Eldawy

Mokbel

et al. 2013

TODS

View full text Add to dashboard Cite

show abstract

“…Chan et al [4] made a compelling case for this, demonstrating that on the NBA basketball dataset (as it was at the time), more than 1 in 20 tuples appear in the skyline in high dimensions. Consequently, there have been numerous efforts to derive a representative subset of smaller size (e.g., [3,13,23,24]), especially one that presents very distinct tuples (e.g., [8,19]) or has a fixed size (e.g., [14,15,21]). …”

Section: Related Workmentioning

confidence: 99%

Computing k-regret minimizing sets

et al. 2014

View full text Add to dashboard Cite

Regret minimizing sets are a recent approach to representing a dataset D by a small subset R of size r of representative data points. The set R is chosen such that executing any top-1 query on R rather than D is minimally perceptible to any user. However, such a subset R may not exist, even for modest sizes, r. In this paper, we introduce the relaxation to k-regret minimizing sets, whereby a top-1 query on R returns a result imperceptibly close to the top-k on D.We show that, in general, with or without the relaxation, this problem is NP-hard. For the specific case of two dimensions, we give an efficient dynamic programming, plane sweep algorithm based on geometric duality to find an optimal solution. For arbitrary dimension, we give an empirically effective, greedy, randomized algorithm based on linear programming. With these algorithms, we can find subsets R of much smaller size that better summarize D, using small values of k larger than 1.

show abstract

“…The user may also face difficulties in balancing between different quality metrics. The skyline presents a good solution for reducing the number of candidate Web services [1], [2] and simplifying the process of selection as it overcomes the major limitation of the current approaches that require users to assign weights over different QoS attributes. The skyline is a subset of Web services that are not(Pareto) dominated by any other Web service.…”

Section: Introductionmentioning

confidence: 99%

QoS-Aware Web Services Selection Based on Fuzzy Dominance

Halfaoui

Fethallah

Didi

2015

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. The selection of an appropriate web service for a particular task has become a difficult challenge due to the increasing number of web services offering similar functionalities. Quality of web services (QoS) becomes crucial for selecting web services among functionally similar components. However, it remains difficult to select an interesting Web services from a large number of candidates with a good compromise between multiples QoS aspect. In this paper, we propose a novel concept based on dominance degree to rank functionally similar services. We rank Web services by using a fuzzification of Pareto dominance called AverageFuzzy-Dominated-Score(AF DetS()). We demonstrate the effectiveness of the AF DetS through a set of simulations by using a real Dataset.

show abstract

Personalized top-k skyline queries in high-dimensional space

Cited by 77 publications

References 29 publications

Flexible and extensible preference evaluation in database systems

Flexible and extensible preference evaluation in database systems

Computing k-regret minimizing sets

QoS-Aware Web Services Selection Based on Fuzzy Dominance

Contact Info

Product

Resources

About