N
Nikos Mamoulis
Researcher at University of Ioannina
Publications - 294
Citations - 12127
Nikos Mamoulis is an academic researcher from University of Ioannina. The author has contributed to research in topics: Joins & Spatial query. The author has an hindex of 56, co-authored 282 publications receiving 11121 citations. Previous affiliations of Nikos Mamoulis include University of Hong Kong & Max Planck Society.
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Proceedings ArticleDOI
Processing and optimization of multiway spatial joins using R-trees
TL;DR: In this article, the authors propose a multiway spatial join algorithm that combines data originated from more than two relations, and apply systematic search algorithms that exploit R-tree to efficiently guide search, without building temporary indexes or materializing intermediate results.
Journal ArticleDOI
Privacy preservation by disassociation
TL;DR: An anonymization technique termed disassociation is proposed that preserves the original terms but hides the fact that two or more different terms appear in the same record, which is the first to employ such a technique to provide protection against identity disclosure in the publication of sparse multidimensional data.
Journal ArticleDOI
Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories
TL;DR: Four intuitive techniques, based on combinations of locations suppression and trajectories, are devised and it is shown that they can prevent privacy breaches while keeping published data accurate for aggregate query answering and frequent subsets data mining.
Journal ArticleDOI
Integration of spatial join algorithms for processing multiple inputs
Nikos Mamoulis,Dimitris Papadias +1 more
TL;DR: This paper analyzes previous work on spatial joins and proposes a novel algorithm, called slot index spatial join (SISJ), that efficiently computes the spatial join between two inputs, only one of which is indexed by an R-tree.
Proceedings ArticleDOI
Capacity constrained assignment in spatial databases
TL;DR: Efficient algorithms foroptimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem are proposed that provide a trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees.