On m-Impact Regions and Standing Top-k Influence Problems

Tang, Bo; Mouratidis, Kyriakos; Han, Mingji

doi:10.1145/3448016.3452832

Cited by 4 publications

(1 citation statement)

References 62 publications

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“…In this scenario, where weights are not well-defined because we are talking about their area of pertinence and not about their specific value, it can happen that a record might dominate another due to its wider boundaries (not limited to a fixed value, as it happens in the traditional way) and then we're allowed to prune the useless tuples. In [36] it is highlighted the fact that the problem belongs to the RP group, since its preference input is a region R and gives as output the top-k results for the query (as points P ).…”

Section: Flexible Skylinementioning

confidence: 99%

Flexible skylines, regret minimization and skyline ranking: a comparison to know how to select the right approach

Fabris¹

2022

Preprint

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Recent studies pointed out some limitations about classic top-k queries and skyline queries. Ranking queries impose the user to provide a specific scoring function, which can lead to the exclusion of interesting results because of the inaccurate estimation of the assigned weights. The skyline approach makes it difficult to always retrieve an accurate result, in particular when the user has to deal with a dataset whose tuples are defined by semantically different attributes. Therefore, to improve the quality of the final solutions, new techniques have been developed and proposed: here we will discuss about the flexible skyline, regret minimization and skyline ranking approaches. We present a comparison between the three different operators, recalling their way of behaving and defining a guideline for the readers so that it is easier for them to decide which one, among these three, is the best technique to apply to solve their problem.

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Section: Flexible Skylinementioning

confidence: 99%