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
Operator Placement for Snapshot Multi-predicate Queries in Wireless Sensor Networks
TL;DR: It is shown that minimizing the communication cost for multi-predicate queries is NP-hard and a dynamic programming algorithm is proposed to compute the optimal solution for small problem instances and the low complexity heuristic algorithm is shown to be scalable and robust to different query characteristics and network size.
Proceedings ArticleDOI
Managing uncertainty in spatial and spatio-temporal data
Reynold Cheng,Tobias Emrich,Hans-Peter Kriegel,Nikos Mamoulis,Matthias Renz,Goce Trajcevski,Andreas Züfle +6 more
TL;DR: This tutorial provides a comprehensive overview of the different challenges involved in managing uncertain spatial and spatio-temporal data and presents state-of-the-art techniques for addressing them.
Journal ArticleDOI
Density-Based Place Clustering Using Geo-Social Network Data
TL;DR: This paper shows how the density-based clustering paradigm can be extended to apply on places which are visited by users of a geo-social network, and considers spatio-temporal information and the social relationships between users who visit the clustered places.
Proceedings ArticleDOI
A revisit to social network-based recommender systems
TL;DR: This paper proposes two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model, and classifies the correlations between pairs of users' ratings.
Book ChapterDOI
Solving Non-binary CSPs Using the Hidden Variable Encoding
Nikos Mamoulis,Kostas Stergiou +1 more
TL;DR: A theoretical and empirical investigation of arc consistency and search algorithms for the hidden variable encoding and it is shown thatSearch algorithms for non-binary constraints can be emulated by corresponding binary algorithms that operate on the hidden Variable encoding and only instantiate original variables.