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 empirical comparison of our algorithm and VS2 in several aspects.
Skyline queries are important in many application domains. In this paper, we propose a novel structure Skyline Diagram, which given a set of points, partitions the plane into a set of regions, referred to as skyline polyominos. All query points in the same skyline polyomino have the same skyline query results. Similar to $k^{th}$-order Voronoi diagram commonly used to facilitate $k$ nearest neighbor ($k$NN) queries, skyline diagram can be used to facilitate skyline queries and many other applications. However, it may be computationally expensive to build the skyline diagram. By exploiting some…
Long-established ranking approaches, such as top-k and skyline queries, have been thoroughly discussed and their drawbacks are well acknowledged. New techniques have been developed in recent years that try to combine traditional ones to overcome their limitations. In this paper we focus our attention on some of them: flexible skylines, regret minimization and skyline ranking queries, because, while these new methods are promising and have shown interesting results, a comparison between them is still not available. After a short introduction of each approach, we discuss analogies and difference…
Topk queries and skyline queries have well explored limitations which recent research have tried to complete through new techniques. In this survey, after resuming such limitations, we consider Restricted Skyline Queries, ORD and ORU approach, Krepresentative minimization queries, Skyline ordering queries, UTK queries approach and Skyrank that aim to overcome them. After introducing and comparing their main concepts, pros and cons, we briefly report the algorithms and confront some of the experimental data collected from the bibliography. To conclude the paper, we summarize the results present…
The techniques most extensively used to retrieve interesting data from data-sets are the Skyline and the Top-k queries. Sadly, they are not enough for facing modern problems, so the needing of something more usable and reliable has come. In this survey we are going to explore Flexible Skylines which are proposed to overcame the old fashion techniques' problems by extending the concept of dominance. After, we are going to compare this approach with the old and new ones evaluating pros and cons. Finally, we will see some interesting applications.
Skyline and Ranking queries have gained great popularity in the recent years. These two techniques are crucial for multi-criteria decision support applications, which are now more popular than ever before. Skyline and Ranking queries are, however, affected by well-known limitations. In the past recent years, the database community provided numerous studies in this field with the aim to overcome the weaknesses of these two approaches. This survey introduces the reader to Skyline and Ranking queries, explaining the concepts on which they are based, with the intent to present the compromise betwe…
Skyline queries are frequently used in data analytics and multi-criteria decision support applications to filter relevant information from big amounts of data. Apache Spark is a popular framework for processing big, distributed data. The framework even provides a convenient SQL-like interface via the Spark SQL module. However, skyline queries are not natively supported and require tedious rewriting to fit the SQL standard or Spark's SQL-like language. The goal of our work is to fill this gap. We thus provide a full-fledged integration of the skyline operator into Spark SQL. This allows for a s…
The question of how to get the best results out of the data we have is an everlasting problem in data science. The two main approaches to tackle the problem are top-k queries and skyline queries. Since their introduction, a new paradigm called flexible skylines has emerged. The aim of this survey is to provide a solid comparison between the new and the old approaches, understanding and exploring their differences and similarities.
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…
Top-k and skylines are two important techniques that can be used to extract the best objects from a set. Both the approaches have well-known pros and cons: a quite big limitation of skyline queries is the impossibility to control the cardinality of the output and the difficulty in specifying a trade-off among attributes, whereas the ranking queries allow so. On the other hand, the usage of ranking implies that ranking functions need to be specified by users and renouncing the simplicity of skylines. Flexible/ restricted skylines present a new approach to tackle this problem, combining the best…
To retrieve the best results in a database we use Top-K queries and Skyline queries but some problems arise. The formers rely too much on user preferences, which are difficult to quantify and may skew the fetching of the data, while the latters tend to output too much data. In this paper, we explore three different branches of research that seek to overcome such limitations: Flexible/Restricted Skylines, Skyline Ordering/Ranking, and Regret Minimization. We analyze how they work and we make comparisons among them to guide the reader to choose the approach that best fits their use cases.
The multi-objective optimization problem has always been the main objective of the principal traditional approaches, such as Ranking queries and Skyline queries. The conventional idea was to either use one or the other, trying to exploit both ranking queries advantages when it comes to taking into account user preferences, and skyline queries points of strength when the main objective was to obtain interesting results from a dataset in a simple, yet effective fashion, both of them showing limitations when entering specific fields of interest.
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…
Top-$k$ queries and skylines are the two most common approaches to finding the most interesting entries in a homogeneous multi-dimensional dataset. However, both of these strategies have some shortcomings. Top-$k$ queries are very challenging to specify precisely and skylines are not customizable to specific scenarios, on top of having unpredictable output cardinalities. We describe some alternative methods aimed at addressing the shortcomings of top-$k$ queries and skylines and compare all approaches to illustrate which of the desired properties each of them possesses.
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…
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 d…
The area of scientific research that deals with the simultaneous optimization of several (possibly conflicting) criteria is named multi-objective optimization. The ability to efficiently filter and extract interesting data out of large datasets is one of the key tasks in modern database systems. This paper provides a general overview of the most common approaches employed to handle the problem in the field of databases, and describes a novel framework named flexible skylines. After analyzing the main differences between single and multi-optimization problems, I will discuss the three main basi…
Given a graph, and a set of query vertices (subset of the vertices), the dynamic skyline query problem returns a subset of data vertices (other than query vertices) which are not dominated by other data vertices based on certain distance measure. In this paper, we study the dynamic skyline query problem on uncertain graphs (DySky). The input to this problem is an uncertain graph, a subset of its nodes as query vertices, and the goal here is to return all the data vertices which are not dominated by others. We employ two distance measures in uncertain graphs, namely, \emph{Majority Distance}, a…
Skyline computation is an increasingly popular query, with broad applicability in domains such as healthcare, travel and finance. Given the recent trend to outsource databases and query evaluation, and due to the proprietary and sometimes highly sensitivity nature of the data (e.g., in healthcare), it is essential to evaluate skylines on encrypted datasets. Several research efforts acknowledged the importance of secure skyline computation, but existing solutions suffer from at least one of the following shortcomings: (i) they only provide ad-hoc security; (ii) they are prohibitively expensive;…
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…
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…
$k$ nearest neighbor ($k$NN) queries and skyline queries are important operators on multi-dimensional data points. Given a query point, $k$NN query returns the $k$ nearest neighbors based on a scoring function such as a weighted sum of the attributes, which requires predefined attribute weights (or preferences). Skyline query returns all possible nearest neighbors for any monotonic scoring functions without requiring attribute weights but the number of returned points can be prohibitively large. We observe that both $k$NN and skyline are inflexible and cannot be easily customized. In this pape…
Nowadays, efficient and effective processing over massive stream data has attracted much attention from the database community, which are useful in many real applications such as sensor data monitoring, network intrusion detection, and so on. In practice, due to the malfunction of sensing devices or imperfect data collection techniques, real-world stream data may often contain missing or incomplete data attributes. In this paper, we will formalize and tackle a novel and important problem, named skyline query over incomplete data stream (Sky-iDS), which retrieves skyline objects (in the presenc…
The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases. Coming with such popular trend is the threat to data privacy, as the cloud gains access to the databases and queries which may contain sensitive information, like medical or financial data. A large body of work has been presented for querying encrypted databases, which has been mostly focused on secure keyword search. In this paper, we instead focus on the support for secure skyline query processing over encrypted o…
The existing algorithms for processing skyline queries cannot adapt to big data. This paper proposes two approximate skyline algorithms based on sampling. The first algorithm obtains a fixed size sample and computes the approximate skyline on the sample. The error of the first algorithm is relatively small in most cases, and is almost independent of the input relation size. The second algorithm returns an $(ε,δ)$-approximation for the exact skyline. The size of sample required by the second algorithm can be regarded as a constant relative to the input relation size, so is the running time. Exp…
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…
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.
It is now cost-effective to outsource large dataset and perform query over the cloud. However, in this scenario, there exist serious security and privacy issues that sensitive information contained in the dataset can be leaked. The most effective way to address that is to encrypt the data before outsourcing. Nevertheless, it remains a grand challenge to process queries in ciphertext efficiently. In this work, we shall focus on solving one representative query task, namely dynamic skyline query, in a secure manner over the cloud. However, it is difficult to be performed on encrypted data as its…
Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical ana…
With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. …
In the XML community, exact queries allow users to specify exactly what they want to check and/or retrieve in an XML document. When they are applied to a semi-structured document or to a document with an overly complex model, the lack or the ignorance of the explicit document model (DTD-Document Type Definition, Schema, etc.) increases the risk of ob-taining an empty result set when the query is too specific, or, too large result set when it is too vague (e.g. it contains wildcards such as "*"). The reason is that in both cases, users write queries according to the document model they have in …
The availability of low cost sensors has led to an unprecedented growth in the volume of spatial data. However, the time required to evaluate even simple spatial queries over large data sets greatly hampers our ability to interactively explore these data sets and extract actionable insights. Graphics Processing Units~(GPUs) are increasingly being used to speedup spatial queries. However, existing GPU-based solutions have two important drawbacks: they are often tightly coupled to the specific query types they target, making it hard to adapt them for other queries; and since their design is base…
Spatial data structures allow to make efficient queries on Geographical Information Systems (GIS). Spatial queries involve the geometry of the data, such as points, lines, or polygons. For instance, a spatial query could poll for the nearest restaurants from a given location. Spatial queries can be solved exhaustively by going through the entire data, which is prohibitive as the number of data points increases. In this article, we are interested in making efficient queries on infinitely long geometrical shapes. For instance, angular sectors, defined as the intersection of two half-spaces, are …
