Blazars are celestial bodies of high interest to astronomers. In particular, through the analysis of photometric and polarimetric observations of blazars, astronomers aim to understand the physics of the blazar's relativistic jet. However, it is challenging to recognize correlations and time variations of the observed polarization, intensity, and color of the emitted light. In our prior study, we proposed TimeTubes to visualize a blazar dataset as a 3D volumetric tube. In this paper, we build primarily on the TimeTubes representation of blazar datasets to present a new visual analytics environment named TimeTubesX, into which we have integrated sophisticated feature and pattern detection techniques for effective location of observable and recurring time variation patterns in long-term, multi-dimensional datasets. Automatic feature extraction detects time intervals corresponding to well-known blazar behaviors. Dynamic visual querying allows users to search long-term observations for time intervals similar to a time interval of interest (query-by-example) or a sketch of temporal patterns (query-by-sketch). Users are also allowed to build up another visual query guided by the time interval of interest found in the previous process and refine the results. We demonstrate how TimeTubesX has been used successfully by domain experts for the detailed analysis of blazar datasets and report on the results.

This paper describes an extendable graphical framework, aflak , which provides a visualization and provenance management environment for the analysis of multi-spectral astronomical datasets. Via its node editor interface, aflak allows the astronomer to compose transforms on input datasets queryable from public astronomical data repositories, then to export the results of the analysis as Flexible Image Transport System (FITS) files, in a manner such that the full provenance of the output data be preserved and reviewable, and that the exported file be usable by other common astronomical analysis software. FITS is the standard of data interchange in astronomy. By embedding aflak ’s provenance data into FITS files, we both achieve interoperability with existing software and full reproducibility of the process by which astronomers make discoveries.

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.

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.

We propose a new application of stochastic point-based rendering, which was recently proposed for implicit surfaces, to large-scale laser-scanned 3D point data. Specifically, we propose a scheme to apply the rendering to transparent and fused visualization of recent large and complex laser-scanned data from cultural assets. Our scheme uses 3D points that are directly acquired using a laser scanner as the rendering primitives. For laser-scanned data that consist of more than 107 or 108 3D points, the pre-processing stage takes only a few minutes, and the rendering stage is executable at interactive frame rates. We do not encounter rendering artifacts originating from the indefiniteness of depth-sorted orders of rendering primitives. Fused visualization with various visual assistants is also possible. We demonstrate the effectiveness of our scheme by visualizing a campus building and a culturally important festival float.