Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an “add-on" on top of a traditional Select-Project-Join query pl...

Skyline queries are a class of preference queries that compute the pareto-optimal tuples from a set of tuples and are valuable for multi-criteria decision making scenarios. While this problem has received significant attention in the context of single relational table, skyline queries over joins of multiple tables that are typical of storage models for RDF data has received much less attention....

We advocate the extension of relational database systems to support preference queries. Many database applications today— from e-commerce to queries over scientific data-sets—are essentially best-match searches. Relational queries are ill-suited for these. Supporting preference criteria in the query language can extend its expresssiveness to cover best-match queries in a natural way. We study s...

In many decision-making applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset. In a high-dimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness...

There has been interest recently in skyline queries, also called Pareto queries, on relational databases. Relational query languages do not support search for “best” tuples, beyond the order by statement. The proposed skyline operator allows one to query for best tuples with respect to any number of attributes as preferences. In this work, we explore what the skyline means, and why skyline quer...

By definition, objects that are skyline points cannot be compared with each other. Yet, thanks to the probabilistic skyline model, skyline points with repeated observations can now be compared. In this model, each object will be assigned a value to denote for its probability of being a skyline point. When we are using this model, some questions will naturally be asked: (1) Which of the objects ...

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...