Visual Scalability Research Articles

Multilevel Visual Analysis of Aggregate Geo-Networks.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.

Visualizing Large-Scale Spatial Time Series with GeoChron.

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.

Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation

Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models’ inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose Random Forest Similarity Map (RFMap), a scalable interactive visual analytics tool designed to analyze RF ensemble models. RFMap focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values. The interactive nature of RFMap allows users to visually interpret model errors and decisions, establishing the necessary confidence and user trust in RF models and improving performance.

Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles.

Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance. Part of this trend, visualization techniques have been extensively used to support classification models' interpretability, with a significant focus on rule-based models. Despite the advances, the existing approaches present limitations in terms of visual scalability, and the visualization of large and complex models, such as the ones produced by the Random Forest (RF) technique, remains a challenge. In this paper, we propose Explainable Matrix (ExMatrix), a novel visualization method for RF interpretability that can handle models with massive quantities of rules. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates, enabling the analysis of entire models and auditing classification results. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability.

A scalable node ordering strategy based on community structure for enhanced temporal network visualization

Temporal networks have been used to map the structural evolution of social, technological, and biological systems, among others. Due to the large amount of information on real-world temporal networks, increasing attention has been given to issues related to the visual scalability of network visualization layouts. However, visual clutter due to edge overlap remains the main challenge calling for efficient methods to improve the visual experience. In this paper, we propose a novel and scalable node reordering approach for temporal network visualization, named Community-based Node Ordering (CNO), combining static community detection with node reordering techniques to enhance the identification of visual patterns. The perception of trends, periodicity, anomalies, and other temporal patterns, is facilitated, resulting in faster decision making. Our method helps not only the study of network activity patterns within communities but also the analysis of relatively large networks by breaking down its structure in smaller parts. Using CNO, we further propose a taxonomy to categorize activity patterns within communities. We performed a number of experiments and quantitative analyses using two real-world networks with distinct characteristics and showed that the proposed layout and taxonomy speed up the identification of patterns that would otherwise be difficult to see.

Visual Analysis of Parallel Interval Events

System logs typically contain lines with time stamps that each describes an event. Where these events semantically form start and end events, they can be combined into interval events. For visual event analytics, the analysis of interval events is more complex than that of point events, since not only the order of events, but also temporal overlaps have to be taken into account. To address this increased complexity and for the purpose of system understanding and analysis, we present SELE, a domain-independent tool for visualizing parallel interval events. SELE is intended to be used on a single long trace of events. A visual technique named strata timeline is developed to handle visual scalability issues. Finally, a multi-core parallel graph searching algorithm is analyzed to demonstrate SELE.

Visual analytics of large dynamic digraphs

In this article, we investigate the problem of visually representing and analyzing large dynamic directed graphs that consist of many vertices, edges, and time steps. With this work we do not primarily focus on graph details but more on achieving an overview about long graph sequences with the major focus to be scalable in vertex, edge, and time dimensions. To reach this goal, we first map each graph to a bipartite layout with vertices in the same order for each graph supporting a preservation of the viewer’s mental map. A sequence of graphs is placed in a left-to-right reading direction. To further reduce link crossings, we draw partial links with user-definable lengths and finally apply edge splatting as a concept to emphasize graph structures by color coding the generated density fields. Time-varying visual patterns can be recognized by inspecting the changes in the color coding in certain regions in the display. We illustrate the usefulness of the approach in two case studies investigating call graphs changing during software development with 21 releases which is a rather short graph sequence but contains several thousand vertices and edges. Visual scalability in the time dimension is shown with more than 1000 graphs from a dynamic social network dataset consisting of face-to-face contacts acquired during the Hypertext 2009 conference recorded by radio-frequency identification badges.

BactoGeNIE: a large-scale comparative genome visualization for big displays.

BackgroundThe volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets.ResultsIn this paper, we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a novel and visually scalable design for comparative gene neighborhood analysis on large display environments. We evaluate BactoGeNIE through a case study on close to 700 draft Escherichia coli genomes, and present lessons learned from our design process.ConclusionsBactoGeNIE accommodates comparative tasks over substantially larger collections of neighborhoods than existing tools and explicitly addresses visual scalability. Given current trends in data generation, scalable designs of this type may inform visualization design for large-scale comparative research problems in genomics.

English

The growing complexity of embedded system hardware and software makes their behaviour analysis a challenging task. In this context, tracing appears to be a promising solution as it provides relevant information about the system execution. However, trace management and analysis are hindered by several issues like the diversity of trace formats, the incompatibility of trace analysis methods, the problem of trace size and its storage as well as by the lack of visualization scalability. In this we present solution for trace management, debug, analysis and testing of embedded systems during development and testing phase. It provides generic solutions for trace storage and defines interfaces and plug in mechanisms for integrating diverse analysis tools even in case of multicore targets.

Freeprocessing: Transparent in situ visualization via data interception.

In situ visualization has become a popular method for avoiding the slowest component of many visualization pipelines: reading data from disk. Most previous in situ work has focused on achieving visualization scalability on par with simulation codes, or on the data movement concerns that become prevalent at extreme scales. In this work, we consider in situ analysis with respect to ease of use and programmability. We describe an abstraction that opens up new applications for in situ visualization, and demonstrate that this abstraction and an expanded set of use cases can be realized without a performance cost.

A hierarchical aggregation model to achieve visualization scalability in the analysis of parallel applications

The analysis of large-scale parallel applications today has several issues, such as the observation and identification of unusual behavior of processes, expected state of the application, and so on. Performance visualization tools offer a wide spectrum of techniques to visually analyze the monitoring data collected from these applications. The problem is that most of the techniques were not conceived to deal with a high number of processes, in large-scale scenarios. A common example for that is the space-time view, largely used in the performance visualization area, but limited on how much data can be analyzed at the same time. The work presented in this article addresses the problem of visualization scalability in the analysis of parallel applications, through a combination of a temporal integration technique, an aggregation model and treemap representations. Results show that our approach can be used to analyze applications composed of several thousands of processes in large-scale and dynamic scenarios.

The Perceptual Scalability of Visualization

Larger, higher resolution displays can be used to increase the scalability of information visualizations. But just how much can scalability increase using larger displays before hitting human perceptual or cognitive limits? Are the same visualization techniques that are good on a single monitor also the techniques that are best when they are scaled up using large, high-resolution displays? To answer these questions we performed a controlled experiment on user performance time, accuracy, and subjective workload when scaling up data quantity with different space-time-attribute visualizations using a large, tiled display. Twelve college students used small multiples, embedded bar matrices, and embedded time-series graphs either on a 2 megapixel (Mp) display or with data scaled up using a 32 Mp tiled display. Participants performed various overview and detail tasks on geospatially-referenced multidimensional time-series data. Results showed that current designs are perceptually scalable because they result in a decrease in task completion time when normalized per number of data attributes along with no decrease in accuracy. It appears that, for the visualizations selected for this study, the relative comparison between designs is generally consistent between display sizes. However, results also suggest that encoding is more important on a smaller display while spatial grouping is more important on a larger display. Some suggestions for designers are provided based on our experience designing visualizations for large displays.

IDGraphs: intrusion detection and analysis using stream compositing

IDGraphs is an interactive visualization system, supporting intrusion detection over massive network traffic streams. It features a novel time-versus-failed-connections mapping that aids in discovery of attack patterns. The number of failed connections (SYN-SYN/ACK) is a strong indicator of suspicious network flows. IDGraphs offers several flow aggregation methods that help reveal different attack patterns. The system also offers high visual scalability through the use of Histographs. The IDGraphs intrusion detection system detects and analyzes a variety of attacks and anomalies, including port scanning, worm outbreaks, stealthy TCP SYN flooding, and some distributed attacks. In this article, we demonstrate IDGraphs using a single day of NetFlow network traffic traces collected at edge routers at Northwestern University which has several OC-3 links.

The JOpera visual composition language

Composing Web services into a coherent application can be a tedious and error-prone task when using traditional textual scripting languages or emerging XML-based approaches. As an alternative, complex interactions patterns and data exchanges between different Web services can be effectively modeled using a visual language. In this paper, we discuss the requirements of such an application scenario and we fully describe the JOpera Visual Composition Language. An extensive set of visual editing tools, a compiler and a debugger for the language have been implemented as part of the JOpera system with the goal of providing a true visual environment for Web service composition with usability features emphasizing rapid development and visual scalability.

Visual Scalability

Visual scalability is the capability of visualization tools effectively to display large datasets, in terms of either the number or the dimension of individual data elements. This article defines and structures the problem of visual scalability, with special emphasis on the role of visualization as a means of access to details of the data. This is done abstractly in terms of responses that measure the business or scientific impact of visualizations and factors that affect the responses, and concretely in terms of measures of visual scalability and factors inuencing them. We assess both current capabilities and future prospects along a number of dimensions. Our approach for increasing visual scalability includes improved visual metaphors, interactivity and perspectives that link multiple views.