Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw [24]). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw's approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.
With the increase in community-contributed data availability, citizens and analysts are interested in identifying patterns, trends and correlation within these datasets. Various levels of aggregation are often applied to interpret such large data schemes. Identifying the proper scales of aggregation is a non-trivial task in this exploratory data analysis process. In this paper, we present an integrated visual analytics environment that facilitates the exploration of multivariate categorical spatiotemporal data at multiple spatial scales of aggregation, focusing on citizen-contributed data. We propose a compact visual correlation representation by embedding various statistical measures across different spatial regions to enable users to explore correlations between multiple data categories across different spatial scales. The system provides several scale-sensitive spatial partitioning strategies to examine the sensitivity of correlations at varying spatial extents. To demonstrate the capabilities of our system, we provide several usage scenarios from various domains including citizen-contributed social media (soundscape ecology) data.
In this article, we present a visual analytics system, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.
This paper investigates the design collaboration process between the Interaction Design (IXD) program and Engineering Projects in Community Service (EPICS) program at Purdue University. There have been quite a few recent research focused on analyzing the collaboration between Industrial Design students and Engineering students. However, little in the literature has been dedicated to discuss the education of Interaction Design and its collaboration with engineering programs. In this research, we tried to identify characteristics that differentiate IXD and EPICS students by focusing on the experiences of the IXD students. Some characteristics made the collaboration successful, but other created frustrations. In order to understand, explore, and evaluate the collaboration process, we collected the data during and after the collaboration and qualitatively analyzed it using a design collaboration model. This analysis identified several interesting features of this interdisciplinary collaboration. The collaboration between two programs benefited both sides and will be conducted regularly. The understanding gained from this study helps to understand and manage the discourse during the interdisciplinary collaboration, which will benefit our future education and inspire better interdisciplinary education and collaboration.
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