Data uncertainty inherently exists in a large number of applications due to factors such as limitations of measuring equipments, update delay, and network bandwidth. Recently, modeling and querying uncertain data have attracted considerable attention from the database community. However, how to perform advanced analysis on uncertain data remains an interesting question. In this paper, we focus ...

In many applications involving multiple criteria optimal decision making, users may often want to make a personal trade-off among all optimal solutions for selecting one object that best fits their personal needs. As a key feature, skyline in a multi-dimensional space provides a minimal set of candidates for such purposes by removing every object that is not preferred by any (monotonic) utility...

Criteria that induce a Skyline naturally represent user’s preference conditions useful to discard irrelevant data in large datasets. However, in the presence of high-dimensional Skyline spaces, the size of the Skyline can still be very large, making unfeasible for users to process this set of points. To identify the best points among the Skyline, the Top-k Skyline approach has been proposed. To...

This thesis deals with a special subset of multi-dimensional set of points, called the Skyline. These points are the maxima or minima of the complete set and are of special interest for the field of decision support. Coming from basic algorithms for computing the Skyline we will develop ideas and algorithms for “on-the-fly” or online computation of the Skyline. We will also extend the concept o...

Given an integer k, a diverse skyline contains the k skyline points that best describe the tradeoffs (among different dimensions) offered by the full skyline. This paper gives an overview of the latest results on this topic. Specifically, we first describe the state-of-the-art formulation of diverse skylines. Then, we explain several algorithms for finding a diverse skyline, where the objective...

Admittedly, despite the plethora of scientometric indices proposed to rank scientists, none of them can fully capture the performance and impact of a scientist, since each index quantifies only one or a few aspects of his/her multifarious performance. Therefore, the task of scientometric ranking can be seen as a multi-dimensional ranking problem, where the different indices comprise the dimensi...

The skyline operator has been studied in database research for multi-criteria decision making. Until now the focus has been on the e ciency or accuracy of single queries. In practice, however, users are increasingly confronted with unknown data collections, where precise query formulation proves di cult. Instead, users explore the data in a sequence of incrementally changing queries to the data...

Skyline queries have emerged as an expressive and informative tool, with minimal user input and thus, they have gained widespread attention. However, previous research works tackle the problem from an efficiency standpoint, i.e., returning the skyline as fast as possible, leaving it to the user to manually inspect the entire skyline result. Clearly, this is impractical, even with a few dozen po...

Given a set of multi-dimensional points, a skyline query returns the interesting points that are not dominated by other points. It has been observed that the actual cardinality (s) of a skyline query result may differ substantially from the desired result cardinality (k), which has prompted studies on how to reduce s for the case where k < s. This paper goes further by addressing the general ca...

Skyline queries are useful in many applications such as multicriteria decision making, data mining, and user preference queries. A skyline query returns a set of interesting data objects that are not dominated in all dimensions by any other objects. For a high-dimensional database, sometimes it returns too many data objects to analyze intensively. To reduce the number of returned objects and to...