Interactive skyline queries

Lee, Jongwuk; You, Gae-won; Hwang, Seung-won; Selke, Joachim; Balke, Wolf‐Tilo

doi:10.1016/j.ins.2012.04.007

Cited by 20 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To overcome the deficiencies of the preference-based queries, some existing studies [2,3,5,6,18,22,31,36,40] involve user interaction. [2,3,18] proposed the interactive skyline query, which tries to reduce the number of skyline tuples in the answer by interacting with the user. However, it only learns the user's preference on the values of attributes (e.g., values red, yellow and blue on the color attribute).…”

Section: Related Workmentioning

confidence: 99%

Interactive Search for One of the Top-k

Wang

Wong

Xie³

2021

Proceedings of the 2021 International Conference on Management of Data

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Interactive Search for One of the Top-k

Wang

Wong

Xie³

2021

Proceedings of the 2021 International Conference on Management of Data

View full text Add to dashboard Cite

show abstract

“…User preferences are dynamic during their data-exploration process [14]; thus, users should be provided with a convenient mode in which they can refine the skyline algorithms by removing certain points, constraining the range of attribute values, or excluding nonessential attributes [7,28]. Furthermore, as user's understanding of the data deepens with data exploration, they may tend to be more interested in certain attributes or data ranges [21,49]. Thus, allowing users to select attributes of interests and highlighting those points that act as the subspace skyline of these attributes is essential.…”

Section: Design Goalsmentioning

confidence: 99%

SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data

Zhao

Cui

et al. 2018

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.

show abstract

“…In a similar spirit, [79] tries to select the k skyline points that best capture the trade-offs among the parameters. Finally, [50] attempts to find a small and focused skyline set. The size of the skyline is reduced by asking from users to state additional preferences.…”

Section: Algorithms For Pareto Aggregationmentioning

confidence: 99%

Finding desirable objects under group categorical preferences

2015

View full text Add to dashboard Cite

Considering a group of users, each specifying individual preferences over categorical attributes, the problem of determining a set of objects that are objectively preferable by all users is challenging on two levels. First, we need to determine the preferable objects based on the categorical preferences for each user, and second we need to reconcile possible conflicts among users' preferences. A naïve solution would first assign degrees of match between each user and each object, by taking into account all categorical attributes, and then for each object combine these matching degrees across users to compute the total score of an object. Such an approach, however, performs two series of aggregation, among categorical attributes and then across users, which completely obscure and blur individual preferences. Our solution, instead of combining individual matching degrees, is to directly operate on categorical attributes, and define an objective Pareto-based aggregation for group preferences. Building on our interpretation, we tackle two distinct but relevant problems: finding the Pareto-optimal objects, and objectively ranking objects with respect to the group preferences. To increase the efficiency when dealing with categorical attributes, we introduce an elegant transformation of categorical attribute values into numerical values, which exhibits certain nice properties and allows us to use well-known index structures to accelerate the solutions to the two problems. In fact, experiments on real and synthetic data show that our index-based techniques are an order of magnitude faster than baseline approaches, scaling up to millions of objects and thousands of users.

show abstract

Interactive skyline queries

Cited by 20 publications

References 31 publications

Interactive Search for One of the Top-k

Interactive Search for One of the Top-k

SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data

Finding desirable objects under group categorical preferences

Contact Info

Product

Resources

About