A flexible solution to embrace Ranking and Skyline queries approaches

Flexible Skyline: one query to rule them all

The most common archetypes to identify relevant information in large datasets and find the bestoptions according to some preferences or user criteria, are the top-k queries (ranking method based ona score function defined over the records attributes) and skyline queries (based on Pareto dominance oftuples). Despite their large diffusion, both approaches have their pros and cons. In this survey paper, a comparison is made between these methods and the Flexible Skylines, which is a framework that combines the ranking and skyline approaches using the novel concept ofF-dominanceto a set of monoton…

面向Skyline查询的排序方法

Ranking Methods for Skyline Queries

针对高基数Skyline结果集缺乏有效对比排序方法的问题，研究者改进dp-idp方法，提出基于PageRank的RankSky、基于TOPSIS的CoSky以及结合多层Skyline的DeepSky，并通过实验验证了相关方法的效果。

Trying to bridge the gap between skyline and top-k queries

There are two most common paradigms that are used in order to identify records of preference in a multi-objective settings, one relies on dominance, like the skyline operator, the other instead, on a utility function defined over the records' attributes, typically using top-k queries. Although they are very popular, we have to take in account their main disadvantages, which bring us to describe three hard requirements: personalization, controllable output size, and flexibility in preference specification. In fact Skyline queries are simple to specify but they are not equipped with any means to…

Comparing modern techniques for querying data starting from top-k and skyline queries

To make intelligent decisions over complex data by discovering a set of interesting options is something that has become very important for users of modern applications. Consequently, researchers are studying new techniques to overcome limitations of traditional ways of querying data from databases as top-k queries and skyline queries. Over the past few years new methods have been developed as Flexible Skylines, Regret Minimization and Skyline ordering/ranking. The aim of this survey is to describe these techniques and some their possible variants comparing them and explaining how they improve…

Flexible skyline: overview and applicability

Ranking (or top-k) and skyline queries are the most popular approaches used to extract interesting data from large datasets. The first one is based on a scoring function to evaluate and rank tuples. Its computation is fast, but it is sensitive to the choice of the evaluating function. Skyline queries are based on the idea of dominance and the result is the set of all non-dominated tuples. This is a very interesting approach, but it can't allow to control the cardinality of the output. Recent researches discovered more techniques to compensate for these drawbacks. In particular, this paper will…

Poisson's CDF applied to Flexible Skylines

The evolution of skyline and ranking queries has created new archetypes like flexible skylines, which have proven to be an efficient method to select relevant data from large datasets using multi objective optimization. This paper aims to study the possible applications of Poisson distribution mass function as a monotonic scoring function in flexible skyline processes, especially those featuring schemas whose attributes can be translated to constant mean rates. Moreover, a method to express users's requirement by means of the F-dominant set of tuples will be proposed using parametrical variati…

Counting stars: a survey on flexible Skyline Query approaches

Nowadays, as the quantity of data to process began to rise, so did the need for a method to discern what pieces of information could be useful for the user; in response, researchers focused their efforts on improving the already existing ranking methods or creating new ones starting from them. This survey will be presented a small list of some of the most known and/or most recent solutions proposed, with some possible applications for them, concerning a state of the art restricted to around the last ten years, comparing their performance with the traditional one top-k and skyline queries.

An outline of multi objective optimization in databases with focus on flexible skyline queries

The problem of optimizing across different, conceivably conflicting, criteria is called multi-objective optimization and it is widely spread across many fields. This is a recurring problem in database queries when there is the need of obtaining the best objects from a very large data set. In this article, I included a complete review of the main approaches typically used to achieve multi-criteria optimization. Starting from ranking queries and skylines and then proceeding to more advanced methods, this paper aims to define a clear outline of multi-objective optimization in databases. In partic…

Spatial Skyline Queries: An Efficient Geometric Algorithm

As more data-intensive applications emerge, advanced retrieval semantics, such as ranking or skylines, have attracted attention. Geographic information systems are such an application with massive spatial data. Our goal is to efficiently support skyline queries over massive spatial data. To achieve this goal, we first observe that the best known algorithm VS2, despite its claim, may fail to deliver correct results. In contrast, we present a simple and efficient algorithm that computes the correct results. To validate the effectiveness and efficiency of our algorithm, we provide an extensive em…

在无随机访问的垂直分布式数据集中计算非支配柔性天际线

Computing the Non-Dominated Flexible Skyline in Vertically Distributed Datasets with No Random Access

针对无随机访问的垂直分布式数据集，提出一种计算非支配柔性天际线（ND）的算法，证明了其正确性与最优性，并通过合成与真实数据集验证了有效性。