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.
Papers
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Proceedings ArticleDOI
Fairness-Aware PageRank
Sotiris Tsioutsiouliklis,Evaggelia Pitoura,Panayiotis Tsaparas,Ilias Kleftakis,Nikos Mamoulis +4 more
TL;DR: In this article, the authors proposed two families of fair Pagerank algorithms: the first (Fairness-Sensitive) algorithm modifies the jump vector to enforce fairness and the second (Locally Fair) algorithm imposes a fair behavior per node.
Journal ArticleDOI
Spatial joins: what's next?
Panagiotis Bouros,Nikos Mamoulis +1 more
TL;DR: The complexity of different data types, the consideration of different join predicates, the use of modern commodity hardware, and support for parallel processing open the road to a number of interesting directions for future research, some of which are outlined in the paper.
Journal ArticleDOI
Efficient Notification of Meeting Points for Moving Groups via Independent Safe Regions
TL;DR: This paper designs efficient algorithms for computing safe regions and examines the shapes of safe regions in the problem’s context and proposes feasible approximations for them, and studies a variant of the problem called the sum-optimal meeting point.
Journal ArticleDOI
In-Memory Interval Joins
TL;DR: This article explores the applicability of a largely ignored forward scan (FS)-based plane sweep algorithm, for single-threaded join evaluation and proposes four optimizations for FS that greatly reduce its cost, making it competitive or even faster than the state-of-the-art.
Proceedings ArticleDOI
Scaling similarity joins over tree-structured data
Yu Tang,Yilun Cai,Nikos Mamoulis +2 more
TL;DR: This paper proposes a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold, and shows that it outperforms the state-of-the-art methods by up to an order of magnitude.