Skyline evaluation techniques (also known as Pareto preference queries) follow a common paradigm that eliminates data elements by finding other elements in a data set that dominate them. Nowadays already a variety of sophisticated skyline evaluation techniques are known, hence skylines are considered a well researched area. On the other hand, the skyline operator does not stand alone in databas...

Given a multi-dimensional dataset of tuples, skyline computation returns a subset of tuples that are not dominated by any other tuples when all dimensions are considered together. Conventional skyline computation, however, is inadequate to answer various queries that need to analyze not just individual tuples of a dataset but also their combinations. In this paper, we study group skyline comput...

Skyline queries have recently attracted a lot of attention for its intuitive query formulation. It can act as a filter to discard sub-optimal objects. However, a major drawback of skyline is that, in datasets with many dimensions, the number of skyline objects becomes large and no longer offer any interesting insights. To solve the problem, recently k-dominant skyline queries have been introduc...

Skyline queries help users make intelligent decisions over complex data. The main shortcoming of skyline queries is that the cardinality of the result set is not known a-priori. To overcome this limitation, the representative skyline query has been proposed, which retrieves a fixed set of k skyline points that best describe all skyline points. Even though the representative skyline has been stu...

As the variant of a skyline query, a reverse skyline query has been studied. However, the existing methods for processing a reverse skyline query have the limitation of service domains and require the high costs of computation to provide various location-based services. In this paper, we propose a new reverse skyline query processing method that efficiently processes a query over the moving obj...

Skyline computation, aiming at identifying a set of skyline points that are not dominated by any other point, is particularly useful for multi-criteria data analysis and decision making. Traditional skyline computation, however, is inadequate to answer queries that need to analyze not only individual points but also groups of points. To address this gap, we generalize the original skyline defin...

We address the problem of maintaining continuous skyline queries efficiently over dynamic objects with d dimensions. Skyline queries are an important new search capability for multi-dimensional databases. In contrast to most of the prior work, we focus on the unresolved issue of frequent data object updates. In this paper we propose the ESC algorithm, an Efficient update approach for Skyline Co...

The skyline query has proven to be an important tool in multi-criteria decision making and search space pruning. A skyline query returns the subset of points from a multidimensional dataset that are not dominated by any other point. Due to its wide applications, skyline query and its variants have been extensively studied in the past. However, skyline computation for incomplete domain, where po...

In recent years, there has been much focus on skyline queries that incorporate and provide more flexible query operators that return data items which are dominating other data items in all attributes (dimensions). Several techniques for skyline have been proposed in the literature. Most of the existing skyline techniques aimed to find the skyline query results by supposing that the values of di...

Although most of the existing skyline queries algorithms focused basically on querying static points through static databases; with the expanding number of sensors, wireless communications and mobile applications, the demand for continuous skyline queries has increased. Unlike traditional skyline queries which only consider static attributes, continuous skyline queries include dynamic attribute...