This research presents an advanced MapReduce-based parallel solution to efficiently address spatial skyline queries on large datasets. In particular, given a set of data points and a set of query points, we first generate the convex hull of the query points in the first MapReduce phase. Then, we propose a novel concept called independent regions, for parallelizing the process of spatial skyline...

Uncertain data are inherent in some important applications. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. In this paper, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic...

Aggregation is among the core functionalities of OLAP systems. Frequently, such queries are issued in decision support systems to identify interesting groups of data. When more than one aggregation function is involved and the notion of interest is not clearly defined, skyline queries provide a robust mechanism to capture the potentially interesting points where (i) users do not need to specify...

In recent years, the skyline query paradigm has been established as a reliable and efficient method for database query personalization. While early efficiency problems have been approached, new challenges in its effectiveness continuously arise. Especially, the rise of the Semantic Web and linked open data leads to personalization issues where skyline queries cannot be applied easily. In fact, ...

A skyline query is useful for extracting a complete set of interesting tuples from a large data set according to multiple criteria. The sizes of data sets are constantly increasing and the architecture of backends are switching from single node environments to cluster oriented setups. Previous work has presented ways to run the skyline query in these setups using the MapReduce framework, but th...

We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SQL can be extended to ...

several open questions. Most pertinently: how do ants encode skyline information? And, how do ants derive a movement direction from the comparison of their current skyline view and their remembered view? Recent experiments, also with M. bagoti [10], have addressed the first question. By masking different portions of the visual panorama, it was possible to show that being able to view prominent ...

Preference-based queries (or skylines) play an important role in cooperative query processing. However, their prohibitive result sizes pose a severe challenge to the paradigm’s practical applicability. Since skyline sizes can only be predicted under strong assumptions, we present a sophisticated user interface to interactively refine queries in case the respective skyline set proves to be too l...

Cooperative database retrieval is a challenging problem: top k retrieval delivers manageable results only when a suitable compensation function (e.g. a weighted mean) is explicitly given. On the other hand skyline queries offer intuitive querying to users, but result set sizes grow exponentially and hence can easily exceed manageable levels. We show how to combine the advantages of skyline quer...