Academic publication archives often draw from numerous, heterogeneous sources, whose records can follow differing naming conventions. As such, ambiguity issues concerning authorship of scientific papers often arise, such as authors sharing similar names, the use of first names versus initials, or alternate name spellings for the same author. These ambiguities have plagued research on scientific collaboration and influence. Detecting and correcting these errors is important for maintaining the archive, as well as for ensuring correctness and reliability in any desired subsequent analysis. There are existing analytic methods designed to accomplish this with varying degrees of accuracy, but many of them require fine tuning or manual categorization. We have developed a visual analytics system to interactively control and apply several analytic name disambiguation algorithms in a finely controlled manner, and to present the results to the user for verification or correction. We demonstrate the efficacy of our system by using it to find and resolve ambiguities in authorship data collected from Cornell University Library's arXiv.org and the InfoVis 2004 contest dataset with improved accuracy and speed over existing approaches.

Information visualization has traditionally limited itself to 2D representations, primarily due to the prevalence of 2D displays and report formats. However, there has been a recent surge in popularity of consumer grade 3D displays and immersive head-mounted displays (HMDs). The ubiquity of such displays enables the possibility of immersive, stereoscopic visualization environments. While techniques that utilize such immersive environments have been explored extensively for spatial and scientific visualizations, contrastingly very little has been explored for information visualization. In this paper, we present our considerations of layout, rendering, and interaction methods for visualizing graphs in an immersive environment. We conducted a user study to evaluate our techniques compared to traditional 2D graph visualization. The results show that participants answered significantly faster with a fewer number of interactions using our techniques, especially for more difficult tasks. While the overall correctness rates are not significantly different, we found that participants gave significantly more correct answers using our techniques for larger graphs.

Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

While virtual reality has been researched in many ways for spatial and scientific visualizations, comparatively little has been explored for visualizations of more abstract kinds of data. In particular, stereoscopic and VR environments for graph visualization have only been applied as limited extensions to standard 2D techniques (e.g. using stereoscopy for highlighting). In this work, we explore a new, immersive approach for graph visualization, designed specifically for virtual reality environments.

Large dynamic graphs occur in many fields. While overviews are often used to provide summaries of the overall structure of the graph, they become less useful as data size increases. Often analysts want to focus on a specific part of the data according to domain knowledge, which is best suited by a bottom-up approach. This paper presents an egocentric, bottom-up method to exploring a large dynamic network using a storyline representation to summarise localized behavior of the network over time.

Parallel applications rely on I/O to load data, store end results, and protect partial results from being lost to system failure. Parallel I/O performance thus has a direct and significant impact on application performance. Because supercomputer I/O systems are large and complex, one cannot directly analyze their activity traces. While several visual or automated analysis tools for large-scale HPC log data exist, analysis research in the high-performance computing field is geared toward computation performance rather than I/O performance. Additionally, existing methods usually do not capture the network characteristics of HPC I/O systems. We present a visual analysis method for I/O trace data that takes into account the fact that HPC I/O systems can be represented as networks. We illustrate performance metrics in a way that facilitates the identification of abnormal behavior or performance problems. We demonstrate our approach on I/O traces collected from existing systems at different scales.

Abstract Large‐scale scientific simulations require execution on parallel computing systems in order to yield useful results in a reasonable time frame. But parallel execution adds communication overhead. The impact that this overhead has on performance may be difficult to gauge, as parallel application behaviors are typically harder to understand than the sequential types. We introduce an animation‐based interactive visualization technique for the analysis of communication patterns occurring in parallel application execution. Our method has the advantages of illustrating the dynamic communication patterns in the system as well as a static image of MPI (Message Passing Interface) utilization history. We also devise a data streaming mechanism that allows for the exploration of very large data sets. We demonstrate the effectiveness of our approach scaling up to 16 thousand processes using a series of trace data sets of ScaLAPACK matrix operations functions.

Monitoring, anomaly detection and forensics are essential tasks that must be carried out routinely for every computer network. The sheer volume of data generated by conventional anomaly detection tools such as Snort often makes it difficult to explain the nature of an attack and track down its source. In this paper we present TVi, a tool that combines multiple visual representations of network traces carefully designed and tightly coupled to support different levels of visual-based querying and reasoning required for making sense of complex traffic data. TVi allows analysts to visualize data starting at a high level, providing information related to the entire network, and easily move all the way down to a very low level, providing detailed information about selected hosts, anomalies and attack paths. We designed TVi with scalability and extensibility in mind: its DBMS foundations make it scalable with virtually no limitations, and other state-of-the-art IDS, like Snort or Bro, can be easily integrated in our tool. We demonstrate with two case studies, a synthetic dataset (DARPA 1999) and a real one (University of Brescia, UniBS, 2009), how TVi can enhance a network administrator's ability to reveal hidden patterns in network traces and link their key information so as to easily reveal details that by merely observing Snort's output would go unnoticed. We make TVi's source code available to the community under an Open Source license.

As a user's digital music collection grows, it can become difficult to navigate. Music library programs aid in this task by organizing music according to tags such as artist or title. However these generally utilize a text based interface, and they do not take into account the content of the music itself. As such, they do not handle untagged or mistagged music well. Automated metrics exist, but are not as widely used since they have the potential to be unreliable. This paper presents a graph-based visual interface for exploring a library of music based on analysis of the content of the music rather than tag information, which allows the user to navigate a music library thematically.

Many graphs used in real-world applications consist of nodes belonging to more than one category. We call such graph ldquomultiple-category graphsrdquo. Social networks are typical examples of multiple-category graphs: nodes are persons, links are friendships, and categories are communities that the persons belong to. It is often helpful to visualize both connectivity and categories of the graphs simultaneously. In this paper, we present a new visualization technique for multiple-category graphs. The technique firstly constructs hierarchical clusters of the nodes based on both connectivity and categories. It then places the nodes by a new hybrid space-filling and force-directed layout algorithm to clearly display both connectivity and category information. We show layout results using our hybrid method and compare it with other methods, and present a case study using an active biological network dataset.

The ability to extract and follow time-varying flow features in volume data generated from large-scale numerical simulations enables scientists to effectively see and validate modeled phenomena and processes. Extracted features often take much less storage space and computing resources to visualize. Most feature extraction and tracking methods first identify features of interest in each time step independently, then correspond these features in consecutive time steps of the data. Since these methods handle each time step separately, they do not use the coherency of the feature along the time dimension in the extraction process. In this paper, we present a prediction-correction method that uses a prediction step to make the best guess of the feature region in the subsequent time step, followed by growing and shrinking the border of the predicted region to coherently extract the actual feature of interest. This method makes use of the temporal-space coherency of the data to accelerate the extraction process while implicitly solving the tedious correspondence problem that previous methods focus on. Our method is low cost with very little storage overhead, and thus facilitates interactive or runtime extraction and visualization, unlike previous methods which were largely suited for batch-mode processing due to high computational cost.

In serial computation, program profiling is often helpful for optimization of key sections of code. When moving to parallel computation, not only does the code execution need to be considered but also communication between the different processes which can induce delays that are detrimental to performance. As the number of processes increases, so does the impact of the communication delays on performance. For large-scale parallel applications, it is critical to understand how the communication impacts performance in order to make the code more efficient. There are several tools available for visualizing program execution and communications on parallel systems. These tools generally provide either views which statistically summarize the entire program execution or process-centric views. However, process-centric visualizations do not scale well as the number of processes gets very large. In particular, the most common representation of parallel processes is a Gantt char t with a row for each process. As the number of processes increases, these charts can become difficult to work with and can even exceed screen resolution. We propose a new visualization approach that affords more scalability and then demonstrate it on systems running with up to 16,384 processes.

Abstract graphs or networks are a commonly recurring data type in many fields. In order to visualize such graphs effectively, the graph must be laid out on the screen coherently. Many algorithms exist to do this, but many of these algorithms tend to be very slow when the input graph is large. This paper presents a new approach to the large graph layout problem, which quickly generates an effective layout. This new method proceeds by generating a clustering hierarchy for the graph, applying a treemap to this hierarchy, and finally placing the graph vertices in their associated regions in the treemap. It is ideal for interactive systems where operations such as semantic zooming are to be performed, since most of the work is done in the initial hierarchy calculation, and it takes very little work to recalculate the layout. This method is also valuable in that the resulting layout can be used as the input to an iterative algorithm (e.g., a force directed method), which greatly reduces the number of iterations required to converge to a near optimal layout.

Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness.

Many methods have been developed for monitoring network traffic, both using visualization and statistics. Most of these methods focus on the detection of suspicious or malicious activities. But what they often fail to do refine and exercise measures that contribute to the characterization of such activities and their sources, once they are detected. In particular, many tools exist that detect network scans or visualize them at a high level, but not very many tools exist that are capable of categorizing and analyzing network scans. This paper presents a means of facilitating the process of characterization by using visualization and statistics techniques to analyze the patterns found in the timing of network scans through a method of continuous improvement in measures that serve to separate the components of interest in the characterization so the user can control separately for the effects of attack tool employed, performance characteristics of the attack platform, and the effects of network routing in the arrival patterns of hostile probes. The end result is a system that allows large numbers of network scans to be rapidly compared and subsequently identified.