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
Lattice Histograms: a Resilient Synopsis Structure
Panagiotis Karras,Nikos Mamoulis +1 more
TL;DR: The lattice histogram is introduced: a novel data reduction method that discovers and exploits any arbitrary hierarchy in the data, and achieves approximation quality provably at least as high as an optimal histogram for any data reduction problem.
Book ChapterDOI
Constraint satisfaction in semi-structured data graphs
Nikos Mamoulis,Kostas Stergiou +1 more
TL;DR: In this article, the authors propose an alternative method that models and solves such queries as constraint satisfaction problems (CSPs) and describe common constraint types occurring in XML queries and show how query evaluation can benefit from methods for preprocessing and solving CSPs.
Journal ArticleDOI
Extracting k most important groups from data efficiently
TL;DR: This work designs the clustered groups algorithm (CGA), which accelerates top-k groups processing for the case where data is clustered by a subset of group-by attributes and develops the recursive hash algorithm (RHA), which applies hashing with early aggregation, coupled with branch-and-bound techniques and derivation heuristics for tight score bounds of hash partitions.
Proceedings ArticleDOI
Parallel and Distributed Processing of Spatial Preference Queries using Keywords
TL;DR: This paper studies the novel problem of parallel and distributed processing of spatial preference queries using keywords, where the input data is stored in a distributed way and proposes parallel algorithms that solve the problem in the MapReduce framework.
Proceedings ArticleDOI
Image similarity retrieval by spatial constraints
TL;DR: This paper focuses on the development of effective methods that take advantage of the special structure of the spatial domain to achieve good average performance even for large images and queries.