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Aristides Gionis
Researcher at Royal Institute of Technology
Publications - 316
Citations - 21244
Aristides Gionis is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Approximation algorithm & Graph (abstract data type). The author has an hindex of 58, co-authored 292 publications receiving 19300 citations. Previous affiliations of Aristides Gionis include Yahoo! & Aalto University.
Papers
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Journal ArticleDOI
XTRACT: Learning Document Type Descriptors from XML Document Collections
TL;DR: XTRACT is proposed, a novel system for inferring a DTD schema for a database of XML documents and its results demonstrate the effectiveness of XTRACT's approach in inferring concise and semantically meaningful DTD schemas for XML databases.
Book ChapterDOI
Estimating number of citations using author reputation
TL;DR: This work shows how to estimate the number of citations for an academic paper using information about past articles written by the same author(s) of the paper using author information and monitoring the items of interest for a short period of time after their creation.
Proceedings ArticleDOI
An optimization framework for query recommendation
TL;DR: This paper presents a formal treatment of the problem of query recommendation, and provides examples of meaningful utility functions to optimize, and discusses how to estimate the effect of recommendations on the reformulation probabilities.
Proceedings ArticleDOI
Mining Large Networks with Subgraph Counting
TL;DR: Data-stream algorithms that approximate the number of all subgraphs of three and four vertices in directed and undirected networks are developed and achieve very good precision in clustering networks with similar structure.
Proceedings ArticleDOI
Finding recurrent sources in sequences
Aristides Gionis,Heikki Mannila +1 more
TL;DR: This work defines the (k,h)-segmentation problem and shows that it is NP-hard in the general case, and gives approximation algorithms achieving approximation ratios of 3 for the L1 error measure and √5 for theL2 error measure, and generalize the results to higher dimensions.