H
Hiroyuki Kitagawa
Researcher at University of Tsukuba
Publications - 393
Citations - 3543
Hiroyuki Kitagawa is an academic researcher from University of Tsukuba. The author has contributed to research in topics: Stream processing & Cluster analysis. The author has an hindex of 21, co-authored 380 publications receiving 3257 citations. Previous affiliations of Hiroyuki Kitagawa include University of Tokyo & Toyohashi University of Technology.
Papers
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Proceedings ArticleDOI
LOCI: fast outlier detection using the local correlation integral
TL;DR: Experiments show that LOCI and aLOCI can automatically detect outliers and micro-clusters, without user-required cut-offs, and that they quickly spot both expected and unexpected outliers.
Book ChapterDOI
TURank: twitter user ranking based on user-tweet graph analysis
TL;DR: In this paper, TURank (Twitter User Rank), which is an algorithm for evaluating users' authority scores in Twitter based on link analysis, is proposed, and experimental results show that the proposed algorithm outperforms existing algorithms.
Proceedings ArticleDOI
Evaluation of signature files as set access facilities in OODBs
TL;DR: This paper proposes a scheme to apply signature file techniques, which were originally invented for text retrieval, to the support of set value accesses, and quantitatively evaluates their potential capabilities.
Journal ArticleDOI
MV-OPES: Multivalued-Order Preserving Encryption Scheme: A Novel Scheme for Encrypting Integer Value to Many Different Values
TL;DR: A novel database encryption scheme called MV-OPES (Multivalued — Order Preserving Encryption Scheme), which allows privacy-preserving queries over encrypted databases with an improved security level and preserves the order of the integer values to allow comparison operations to be directly applied on encrypted data.
Extracting Mobility Statistics from Indexed Spatio-Temporal Datasets
TL;DR: An algorithm to extract mobility statistics from indexed spatio-temporal datasets for the interactive analysis of huge collections of moving object trajectories by focusing on a mobility statistics value called the Markov transition probability.