Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data. However, when data is a live streaming feed, conventional DR methods cannot be directly used because of their computational complexity and inability to preserve the projected data positions at previous time points. In addition, the problem becomes even more challenging when the dynamic data records have a varying number of dimensions as often found in real-world applications. This paper presents an incremental DR solution. We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data. First, we use geometric transformation and animation methods to help preserve a viewer's mental map when visualizing the incremental results. Second, to handle data dimension variants, we use an optimization method to estimate the projected data positions, and also convey the resulting uncertainty in the visualization. We demonstrate the effectiveness of our design with two case studies using real-world datasets.

Abstract The inconceivable ability and common practice to collect personal data as well as the power of data‐driven approaches to businesses, services and security nowadays also introduce significant privacy issues. There have been extensive studies on addressing privacy preserving problems in the data mining community but relatively few have provided supervised control over the anonymization process. Preserving both the value and privacy of the data is largely a non‐trivial task. We present the design and evaluation of a visual interface that assists users in employing commonly used data anonymization techniques for making privacy preserving visualizations. Specifically, we focus on event sequence data due to its vulnerability to privacy concerns. Our interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm and fine‐tune the results per their discretion. Multiple use case scenarios demonstrate the utility of our design. A user study similarly investigates the effectiveness of the privacy preserving strategies. Our results show that using a visual‐based interface is effective for identifying potential privacy issues, for revealing underlying anonymization processes, and for allowing users to balance between data utility and privacy.

Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques-along with evaluating how applying the strategies will affect the utility of the anonymized results-remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.

Studies on augmenting visualization with sound are typically based on the assumption that sound can be complementary and assist in data analysis tasks. While sound promotes a different sense of engagement than vision, we conjecture that by augmenting non-speech audio to a visualization can not only help enhance the users' perception of the data but also increase their engagement with the data exploration process. We have designed a preliminary user study to test users' performance and engagement while exploring in a data visualization system under two different settings: visual-only and audiovisual. For our study, we used basketball player movement data in a game and created an interactive visualization system with three linked views. We supplemented sound to the visualization to enhance the users' understanding of a team's offensive/defensive behavior. The results of our study suggest that we need to better understand the effect of sound choice and encoding before considering engagement. We also find that sound can be useful to draw novice users' attention to patterns or anomalies in the data. Finally, we propose follow-up studies with designs informed by the findings from this study.

Researchers such as sociologists create visualizations of multivariate node-link diagrams to present findings about the relationships in communities. Unfortunately, such visualizations can inadvertently expose the ostensibly private identities of the persons that make up the dataset. By purposely violating graph readability metrics for a small region of the graph, we conjecture that local, exposed privacy leaks may be perceptually masked from easy recognition. In particular, we consider three commonly known metrics-edge crossing, node clustering, and node-edge overlapping-as a strategy to hide leaks. We evaluate the effectiveness of violating these metrics by conducting a user study that measures subject performance at visually searching for and identifying a privacy leak. Results show that when more masking operations are applied, participants needed more time to locate the privacy leak, though exhaustive, brute force search can eventually find it. We suggest future directions on how perceptual masking can be a viable strategy, primarily where modifying the underlying network structure is unfeasible.

Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.

Analyzing social network data helps sociologists understand the behaviors of individuals and groups as well as the relationships between them. With additional ontology information, the semantics behind the network structure can be further explored. Unfortunately, creating network visualizations with these datasets for presentation can inadvertently expose the private and sensitive information of individuals that reside in the data. To deal with this problem, we generalize conventional data anonymization models (originally designed for relational data) and formally apply them in the context of privacy preserving ontological network visualization. We use these models to identify the privacy leaks that exist in a visualization, provide graph modification actions that remove and/or perceptually minimize the effect of the identified leaks, and discuss strategies for what types of privacy actions to choose depending on the context of the leaks. We implement an ontological visualization interface with associated privacy preserving operations, and demonstrate with two case studies using real-world datasets to show that our approach can identify and solve potential privacy issues while balancing overall graph readability and utility.

Neuroscientists study brain functional connectivity in order to obtain a deeper understanding of how the brain functions. Current studies are mainly based on analyzing the averaged brain connectivity of a group (or groups) due to the high complexity of the collected data in terms of dimensionality, variability, and volume. While it is more desirable for the researchers to explore the potential variability between individual subjects or groups, a data analysis solution meeting this need is absent. In this paper, we present the design and capabilities of such a visual analytics system, which enables neuroscientists to visually compare the differences of brain networks between individual subjects as well as group averages, to explore a large dataset and examine sub-groups of participants that may not have been expected a priori to be of interest, to review detailed information as needed, and to manipulate the data and views to fit their analytical needs with easy interactions. We demonstrate the utility and strengths of this system with case studies using a representative functional connectivity dataset.

This paper presents a comparative study on personal visualizations of bibliographic data. We consider three designs for egocentric visualization: node-link diagrams, adjacency matrices, and botanical trees to depict one's academic career in terms of his/her publication records. Case studies are conducted to compare the effectiveness of resulting visualizations for conveying particular aspect of a researcher's bibliographic records. Based on our study, we find that node-link diagrams are better at revealing the overall distribution of certain attributes; adjacency matrices can convey more information with less clutter; and botanical trees are visually attractive and provide the best at a glance characterization of the mapped data, but mapping data to tree features must be carefully done to derive expressive visualization.

While the assessment of using motion in visualizations has been polarized, we conjecture that motion may be more effective in comparative visualizations if applied properly, especially when dealing with large amounts of multi-dimensional data. We have designed visualizations to represent driver behaviors. A series of user studies have been conducted to verify if adding motion to the static visualization can help users make comparisons and separate drastically different behaviors. Results show that adding motion indeed leads to shorter completion time and less cognitive workload.

There has been a long-standing question of whether sound or audio annotations may assist in data visualization tasks. This paper presents our study focusing on enhancing timeline/storyline like visualizations with audio annotations. Timeline visualizations are widely used to illustrate interactions among different entities over time. This type of visualizations facilitate reviewing important activities and their associations. For a long timeline, however, it is difficult for the viewer to remember all the essential information found in the visualization process. Since hearing is another primary human sense for perceiving information, we conjecture that adding audio annotations to selected sections of a timeline visualization can help improve the viewer's recall of important events. We have designed a user study based on augmenting storyline visualizations with verbal text, and tested subjects with three different settings: visual cues only, verbal text only, and both. While our test results do not give a strong indication of the clear advantage of adding verbal text, the lessons learned in our study suggest directions for further study and will help us and others design audio-augmented visualization systems.

Abstract Reviewing literatures for a certain research field is always important for academics. One could use Google‐like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross‐referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user‐friendly interface to help users quickly grasp the intrinsic complex citation‐reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node‐link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.