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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Journal ArticleDOI
Aggregate nearest neighbor queries in road networks
TL;DR: This work considers alternative aggregate functions and techniques that utilize Euclidean distance bounds, spatial access methods, and/or network distance materialization structures and shows that their relative performance depends on the problem characteristics.
Journal ArticleDOI
An efficient and scalable algorithm for clustering XML documents by structure
TL;DR: This work proposes a hierarchical algorithm (S-GRACE) for clustering XML documents based on structural information in the data, and proposes a computationally efficient distance metric defined between documents and sets of documents using the notion of structure graph (s-graph).
Proceedings Article
Efficient processing of top- k dominating queries on multi-dimensional data
Man Lung Yiu,Nikos Mamoulis +1 more
TL;DR: The top-k dominating query as mentioned in this paper returns k data objects which dominate the highest number of objects in a dataset, which is an important tool for decision support since it provides data analysts an intuitive way for finding significant objects.
Proceedings ArticleDOI
Meta Structure: Computing Relevance in Large Heterogeneous Information Networks
TL;DR: This paper proposes to use meta structure, which is a directed acyclic graph of object types with edge types connecting in between, to measure the proximity between objects, and develops three relevance measures based on meta structure.
Journal ArticleDOI
Discovery of Periodic Patterns in Spatiotemporal Sequences
TL;DR: This paper defines the problem of mining periodic patterns in spatiotemporal data and proposes an effective and efficient algorithm for retrieving maximal periodic patterns, and demonstrates how the mining technique can be adapted for two interesting variants of the problem.