2017 Fiscal Year
International Academic Journal
International Conference Proceedings
TimeTubes: Visual Fusion and Validation for Ameliorating Uncertainties of Blazar Datasets from Different Observatories
in Proceedings of the Computer Graphics International Conference,
Article No. 14, Yokohama (Japan), 2017
Article No. 14, Yokohama (Japan), 2017
Astronomers have been observing blazars to solve the mystery of the relativistic jet. A technique called TimeTubes uses a 3D volumetric tube to visualize the time-dependent multivariate observed datasets and allows astronomers to interactively analyze the dynamic behavior of and relationship among those variables. However, the observed datasets themselves exhibit uncertainty due to their errors and missing periods, whereas periods interpolated by TimeTubes result in a different type of uncertainty. In this paper, we present a technique for ameliorating such data- and mapping-inherent uncertainties: visual fusion of datasets for the same blazar from two different observatories. Visual data fusion with Time-Tubes enables astronomers to validate the datasets in a meticulous manner.
Making Many-to-Many Parallel Coordinate Plots Scalable by Asymmetric Biclustering
in Proceedings of IEEE Pacific Visualization 2017, Seoul (Korea), 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)
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.