Abstract Displaying small multiples is a popular method for visually summarizing and comparing multiple facets of a complex data set. If the correlations between the data are not considered when displaying the multiples, searching and comparing specific items become more difficult since a sequential scan of the display is often required. To address this issue, we introduce CorrelatedMultiples, a spatially coherent visualization based on small multiples, where the items are placed so that the distances reflect their dissimilarities. We propose a constrained multi‐dimensional scaling (CMDS) solver that preserves spatial proximity while forcing the items to remain within a fixed region. We evaluate the effectiveness of our approach by comparing CMDS with other competing methods through a controlled user study and a quantitative study, and demonstrate the usefulness of CorrelatedMultiples for visual search and comparison in three real‐world case studies.

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

As text streams become increasingly available from social media such as Facebook and Twitter, visual analysis of streaming text data is playing an important role in most business sectors. A fundamental challenge in visualizing a large amount of streaming text data is to preserve the user's mental map to enable tracking dynamic changes in topics, while simultaneously utilizing the display space efficiently. In this paper, we present CompactMap, an online visual interface that packs text clusters efficiently, with stable updates to maintain the user's mental map. It achieves spatiotemporally coherent layouts by dynamically matching clusters across time, and removing cluster overlaps according to spatial proximity and constraints. We developed a visual search engine based on CompactMaps for exploring a large amount of text streams in details on demand. We demonstrate the effectiveness of our approach in a controlled user study compared with a competing method.

In some applications of graph visualization, input edges have associated target lengths. Dealing with these lengths is a challenge, especially for large graphs. Stress models are often employed in this situation. However, the traditional full stress model is not scalable due to its reliance on an initial all-pairs shortest path calculation. A number of fast approximation algorithms have been proposed. While they work well for some graphs, the results are less satisfactory on graphs of intrinsically high dimension, because some nodes may be placed too close together, or even share the same position. We propose a solution, called the maxent-stress model, which applies the principle of maximum entropy to cope with the extra degrees of freedom. We describe a force-augmented stress majorization algorithm that solves the maxent-stress model. Numerical results show that the algorithm scales well, and provides acceptable layouts for large, nonrigid graphs. This also has potential applications to scalable algorithms for statistical multidimensional scaling (MDS) with variable distances.

In some applications of graph visualization, input edges have associated target lengths. Dealing with these lengths is a challenge, especially for large graphs. Stress models are often employed in this situation. However, the traditional full stress model is not scalable due to its reliance on an initial all-pairs shortest path calculation. A number of fast approximation algorithms have been proposed. While they work well for some graphs, the results are less satisfactory on graphs of intrinsically high dimension, because nodes overlap unnecessarily. We propose a solution, called the maxent-stress model, which applies the principle of maximum entropy to cope with the extra degrees of freedom. We describe a force-augmented stress majorization algorithm that solves the maxent-stress model. Numerical results show that the algorithm scales well, and provides acceptable layouts for large, non-rigid graphs. This also has potential applications to scalable algorithms for statistical multidimensional scaling (MDS) with variable distances.

Anonymized Call Detail Records (CDRs) contain positional information of large populations and therefore have been extensively analyzed to understand human mobility. Due to the temporally sparse and spatially coarse nature of the data, most of these studies have focused on primitive aspects of movements such as travel distance and speed. Incorporating underlying geographic information in these analyses would allow analysts to put these movements into context and to gain deeper insight into how metropolitan areas function. In this paper, we present a set of procedures for inferring mobile users' mobility patterns while retaining the context of underlying geography. We apply these methods to our case study on New York City anonymized CDRs. We find that our methods verify current areal semantics and commuting rush-hour patterns, and we also derive further implications regarding geographic, demographic, and other effects on human mobility.

Graphs are often used to encapsulate relationships between objects. Node-link diagrams, commonly used to visualize graphs, suffer from visual clutter on large graphs. Edge bundling is an effective technique for alleviating clutter and revealing high-level edge patterns. Previous methods for general graph layouts either require a control mesh to guide the bundling process, which can introduce high variation in curvature along the bundles, or all-to-all force and compatibility calculations, which is not scalable. We propose a multilevel agglomerative edge bundling method based on a principled approach of minimizing ink needed to represent edges, with additional constraints on the curvature of the resulting splines. The proposed method is much faster than previous ones, able to bundle hundreds of thousands of edges in seconds, and one million edges in a few minutes.

Greetings from the program committee of Pacific Visualization 2010. This was the third year of the conference. The quality of this year's program conveys exciting work that is being done in many areas of visualization. In the few years since the inception of the meeting, there has been a steady increase in the number of submissions and their overall technical strength. This reflects the maturity of the field, and perhaps increasing support for a spring meeting to complement VisWeek held in the fall.

Mobile devices are ubiquitously used to access web applications. Multimodal mobile interfaces can offer advantages over less flexible approaches, in both usability and range of features. In this study we consider applying speech input to a web-based network management service. The key issue we are interested in is how to perform suitable multidimensional search through web-based interface on mobile devices. We present results from a pilot user evaluation, focusing on the comparison of a novel speech input method with the existing manual (GUI, Graphical User Interface) input for AT&T's Visualizer management service, on an iPhone. Speech input was experimentally shown to be as effective, more efficient, and preferred over GUI input by most users. We foresee that a multimodal approach may be preferable for many applications on mobile devices.

We present LiveRAC, a visualization system that supports the analysis of large collections of system management time-series data consisting of hundreds of parameters across thousands of network devices. LiveRAC provides high information density using a reorderable matrix of charts, with semantic zooming adapting each chart's visual representation to the available space. LiveRAC allows side-by-side visual comparison of arbitrary groupings of devices and parameters at multiple levels of detail. A staged design and development process culminated in the deployment of LiveRAC in a production environment. We conducted an informal longitudinal evaluation of LiveRAC to better understand which proposed visualization techniques were most useful in the target environment.

Measuring distance or some other form of proximity between objects is a standard data mining tool. Connection subgraphs were recently proposed as a way to demonstrate proximity between nodes in networks. We propose a new way of measuring and extracting proximity in networks called “cycle-free effective conductance” (CFEC). Importantly, the measured proximity is accompanied with a proximity subgraph which allows assessing and understanding measured values. Our proximity calculation can handle more than two endpoints, directed edges, is statistically well behaved, and produces an effectiveness score for the computed subgraphs. We provide an efficient algorithm to measure and extract proximity. Also, we report experimental results and show examples for four large network datasets: a telecommunications calling graph, the IMDB actors graph, an academic coauthorship network, and a movie recommendation system.

The Internet has become a wild place: malicious code is spread on personal computers across the world, deploying botnets ready to attack the network infrastructure. The vast number of security incidents and other anomalies overwhelms attempts at manual analysis, especially when monitoring service provider backbone links. We present an approach to interactive visualization with a case study indicating that interactive visualization can be applied to gain more insight into these large data sets. We superimpose a hierarchy on IP address space, and study the suitability of Treemap variants for each hierarchy level. Because viewing the whole IP hierarchy at once is not practical for most tasks, we evaluate layout stability when eliding large parts of the hierarchy, while maintaining the visibility and ordering of the data of interest.

Measuring distance or some other form of proximity between objects is a standard data mining tool. Connection subgraphs were recently proposed as a way to demonstrate proximity between nodes in networks. We propose a new way of measuring and extracting proximity in networks called "cycle free effective conductance"(CFEC). Our proximity measure can handle more than two endpoints, directed edges, is statistically well-behaved, and produces an effectiveness score for the computed subgraphs. We provide an efficien talgorithm. Also, we report experimental results and show examples for three large network data sets: a telecommunications calling graph, the IMDB actors graph, and an academic co-authorship network.

In many applications, data is collected and indexed by geo-spatial location. Discovering interesting patterns through visualization is an important way of gaining insight about such data. A previously proposed approach is to apply local placement functions such as PixelMaps that transform the input data set into a solution set that preserves certain constraints while making interesting patterns more obvious and avoid data loss from overplotting. In experience, this family of spatial transformations can reveal fine structures in large point sets, but it is sometimes difficult to relate those structures to basic geographic features such as cities and regional boundaries. Recent information visualization research has addressed other types of transformation functions that make spatially-transformed maps with recognizable shapes. These types of spatial-transformation are called global shape functions. In particular, cartogram-based map distortion has been studied. On the other hand, cartogram-based distortion does not handle point sets readily. In this study, we present a framework that allows the user to specify a global shape function and a local placement function. We combine cartogram-based layout (global shape) with PixelMaps (local placement), obtaining some of the benefits of each toward improved exploration of dense geo-spatial data sets.

The field of security visualization is in need of a paradigm shift in order to allow visualization tools to be practically used by security engineers. Security engineers must complete two different tasks, that of Discovery of a pattern, and that of Searching for a pattern in a data set. Current security visualizations do not aid the user in creating symbolic rules that represent visual patterns. Transforming visual patterns to symbolic rules requires effort by the security engineer and detracts from their main task of discovering interesting patterns. In this paper we describe the idea of Closing-The-Loop - a system where symbolic rules are created from visual patterns.

Graph drawing is a basic visualization tool that works well for graphs having up to hundreds of nodes and edges. At greater scale, data density and occlusion problems often negate its effectiveness. Conventional pan-and-zoom, multiscale, and geometric fisheye views are not fully satisfactory solutions to this problem. As an alternative, we propose a topological zooming method. It precomputes a hierarchy of coarsened graphs that are combined on-the-fly into renderings, with the level of detail dependent on distance from one or more foci. A related geometric distortion method yields constant information density displays from these renderings.

Three papers in this issue of TVCG are expanded versions of ones presented at the IEEE Symposium on Information Visualization (InfoVis) 2003. These examples convey the breadth of work in the InfoVis community, attacking practically motivated problems from sophisticated mathematical and scientific perspectives. They demonstrate an interaction technique for navigation, a mathematically well-founded approach to the difficult problem of dimensionality reduction, and an evaluation of a particular channel of visual perception. These are briefly summarized.

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. In this study, we formally define a family of cartogram drawing problems. We show that even simple variants are unsolvable in the general case. Because the feasible variants are NP-complete, heuristics are needed to solve the problem. Previously proposed solutions suffer from problems with the quality of the generated drawings. For a cartogram to be recognizable, it is important to preserve the global shape or outline of the input map, a requirement that has been overlooked in the past. To address this, our objective function for cartogram drawing includes both global and local shape preservation. To measure the degree of shape preservation, we propose a shape similarity function, which is based on a Fourier transformation of the polygons' curvatures. Also, our application is visualization of dynamic data, for which we need an algorithm that recalculates a cartogram in a few seconds. None of the previous algorithms provides adequate performance with an acceptable level of quality for this application. In this paper, we therefore propose an efficient iterative scanline algorithm to reposition edges while preserving local and global shapes. Scanlines may be generated automatically or entered interactively to guide the optimization process more closely. We apply our algorithm to several example data sets and provide a detailed comparison of the two variants of our algorithm and previous approaches.

PixelMaps are a new pixel-oriented visual data mining technique for large spatial datasets. They combine kernel-density-based clustering with pixel-oriented displays to emphasize clusters while avoiding overlap in locally dense point sets on maps. Because a full evaluation of density functions is prohibitively expensive, we also propose an efficient approximation, Fast-PixelMap, based on a synthesis of the quadtree and gridfile data structures.

The construction industry traditionally uses two dimensional visualisation techniques to analyse project progress. This usually takes the form of an 's-curve', so called because of the characteristic plot shape (anecdotally generated by resource application take-up in many projects). Analysing project progress in this manner provides a 'single view' guide to data trends. The building blocks of projects are 'tasks'. Deeper interpretation of a project might require studying individual s-curves for each task. Indicative patterns can go unrecognised in this mass of unfiltered visual data. 'Procession' is an information visualisation software tool. It has been developed to provide a more comprehensive representation of project progress, utilising a three-dimensional data surface. In addition to offering a 'single view' navigable data-surface, Procession also intelligently filters and emphasises tasks, according to the significance of their deviant performance parameters.

We describe a package of practical tools and libraries for manipulating graphs and their drawings. Our design, which is aimed at facilitating the combination of the package components with other tools, includes stream and event interfaces for graph operations, high-quality static and dynamic layout algorithms, and the ability to handle sizeable graphs. We conclude with a description of the applications of this package to a variety of software engineering tools. Copyright © 2000 John Wiley & Sons, Ltd.

No abstract available.

Visual exploration of massive datasets arising from telecommunication networks and services is a challenge. This paper describes SWIFT-3D, an integrated data visualization and exploration system created at AT&T Labs for large scale network analysis. SWIFT-3D integrates a collection of interactive tools that includes pixel-oriented 2D maps, interactive 3D maps, statistical displays, network topology diagrams and an interactive drill-down query interface. Example applications are described, demonstrating a successful application to analyze unexpected network events (high volumes of unanswered calls), and comparison of usage of an Internet service with voice network traffic and local access coverage.

Although routing is a well-studied problem in various contexts, there remain unsolved problems in routing edges for graph layouts. In contrast with techniques from other domains such as VLSI CAD and robotics, where physical constraints play a major role, aesthetics play the more important role in graph layout. For graphs, we seek paths that are easy to follow and add meaning to the layout. We describe a collection of aesthetic attributes applicable to drawing edges in graphs, and present a general approach for routing individual edges subject to these principles. We also give implementation details and survey difficulties that arise in an implementation. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A four-pass algorithm for drawing directed graphs is presented. The fist pass finds an optimal rank assignment using a network simplex algorithm. The seconds pass sets the vertex order within ranks by an iterative heuristic, incorporating a novel weight function and local transpositions to reduce crossings. The third pass finds optimal coordinates for nodes by constructing and ranking an auxiliary graph. The fourth pass makes splines to draw edges. The algorithm creates good drawings and is fast.>

No abstract available.

Article Free Access Share on A method for drawing graphs Authors: R. J. Lipton Princeton University, Princeton, NJ Princeton University, Princeton, NJView Profile , S. C. North AT&T Bell Laboratories, Murray Hill, New Jersey AT&T Bell Laboratories, Murray Hill, New JerseyView Profile , J. S. Sandberg AT&T Bell Laboratories, Murray Hill, New Jersey AT&T Bell Laboratories, Murray Hill, New JerseyView Profile Authors Info & Claims SCG '85: Proceedings of the first annual symposium on Computational geometryJune 1985 Pages 153–160https://doi.org/10.1145/323233.323254Online:01 June 1985Publication History 40citation611DownloadsMetricsTotal Citations40Total Downloads611Last 12 Months14Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The first component of a VLSI (very large-scale integration) design environment being built at Princeton University is described.The general theme of this effort is to make the design of VLSI circuits as similar to programming as possible.The attempt is to build tools that do for the VLSI circuit designer what the best software tools do for the implementer of large software systems.

ALI is a procedural language to specify VLSI layouts. It allows the designer to describe layouts without reference to the sizes and positions of the layout elements or to the distances between them. Among the interesting characteristics of ALI are that it does not need design rule checking, is easy to extend, facilitates the division of labor and permits the easy update of a layout to new design rules or to new processes. The general features of the language and the experience gained with a preliminary implementation of it are described.