業績

2017 Fiscal Year

International Academic Journal

International Conference Proceedings

Hsiang-Yun Wu, Yusuke Niibe, Kazuho Watanabe, Shigeo Takahashi, Makoto Uemura, Issei Fujishiro
Making Many-to-Many Parallel Coordinate Plots Scalable by Asymmetric Biclustering
in Proceedings of IEEE Pacific Visualization 2017
Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension variables or subsets of the data samples, is required to describe the essence hidden in the original dataset. This paper proposes a visual data mining framework for supporting semiautomatic data analysis that builds upon asymmetric biclustering to explore highly correlated feature subspaces. For this purpose, a variant of parallel coordinate plots, many-to-many parallel coordinate plots, is extended to visually assist appropriate selections of feature subspaces as well as to avoid intrinsic visual clutter. In this framework, biclustering is applied to dimension variables and data samples of the dataset simultaneously and asymmetrically. A set of variable axes are projected to a single composite axis while data samples between two consecutive variable axes are bundled using polygonal strips. This makes the visualization method scalable and enables it to play a key role in the framework. The effectiveness of the proposed framework has been empirically proven, and it is remarkably useful for many-to-many parallel coordinate plots.

International Conference Posters (Peer-Reviewed)

Malik Olivier Boussejra, Noboru Adachi, Hideki Shojo, Ryohei Takahashi, Issei Fujishiro
LMML: Describing injuries for forensic data visualization
to appear as a poster in Proceedings of SAS NICOGRAPH International 2016, Hangzhou (China), July 2016
Fighting against crime is paramount to any society, maybe more today than ever before. Tools to fight and elucidate crime are rooted in forensic science. Through the autopsy of a body, we can answer a whole range of questions as to how death happened and come up with explanations and counter-measures so that the same dire circumstance does not happen again. Now, because the reports collecting the data are written manually, the recording of the data collected through traditional autopsy still is a cumbersome, time-consuming task. Our framework, based on a mark-up language (that we dubbed ”LMML”) to store, describe and arrange forensic data, aims at overcoming those issues. Our contribution is twofold: the design of the syntax and semantics of LMML, and the conception of an interface to create, edit, analyse or query files written in that language. Thus, this framework allows quicker, smoother input of forensic data, for better automation and visualization thereof, so that they can be used by medical examiners, investigators, as well as judicial courts.

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Oral Presentation