Modeling visual interactive systems through dynamic visual languages

Visual interaction processes are modeled in this paper as sequences of visual sentences in which for each visual sentence only a limited set of user actions is possible. We introduce the notion of "dynamic visual language" as a weakly ordered set of visual sentences characterized by the presence of common elements. We present a formal model of derivation of visual sentences in a dynamic visual language in which each visual sentence specifies the possible actions which can be performed on it and the possible transformations it can go through. In this way, we offer a formal setting in which the interaction process can be formally specified. A user interface can be derived from the formal specification, so that it embeds proper context elements which limit user disorientation. The concepts are illustrated by the user interaction with a prototype of a digital library developed at the University of Bari.     

Graph signatures for visual analytics

We present a visual analytics technique to explore graphs using the concept of a data signature. A data signature, in our context, is a multidimensional vector that captures the local topology information surrounding each graph node. Signature vectors extracted from a graph are projected onto a low-dimensional scatterplot through the use of scaling. The resultant scatterplot, which reflects the similarities of the vectors, allows analysts to examine the graph structures and their corresponding real-life interpretations through repeated use of brushing and linking between the two visualizations. The interpretation of the graph structures is based on the outcomes of multiple participatory analysis sessions with intelligence analysts conducted by the authors at the Pacific Northwest National Laboratory. The paper first uses three public domain data sets with either well-known or obvious features to explain the rationale of our design and illustrate its results. More advanced examples are then used in a customized usability study to evaluate the effectiveness and efficiency of our approach. The study results reveal not only the limitations and weaknesses of the traditional approach based solely on graph visualization, but also the advantages and strengths of our signature-guided approach presented in the paper     

High-dimensional visual analytics: interactive exploration guided by pairwise views of point distributions

We introduce a method for organizing multivariate displays and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional Euclidean space. These characterizations include such measures as density, skewness, shape, outliers, and texture. Statistical analysis of these measures leads to ways for 1) organizing 2D scatterplots of points for coherent viewing, 2) locating unusual (outlying) marginal 2D distributions of points for anomaly detection and 3) sorting multivariate displays based on high-dimensional data, such as trees, parallel coordinates, and glyphs     

A visual analytics agenda

Researchers have made significant progress in disciplines such as scientific and information visualization, statistically based exploratory and confirmatory analysis, data and knowledge representations, and perceptual and cognitive sciences. Although some research is being done in this area, the pace at which new technologies and technical talents are becoming available is far too slow to meet the urgent need. National Visualization and Analytics Center's goal is to advance the state of the science to enable analysts to detect the expected and discover the unexpected from massive and dynamic information streams and databases consisting of data of multiple types and from multiple sources, even though the data are often conflicting and incomplete. Visual analytics is a multidisciplinary field that includes the following focus areas: (i) analytical reasoning techniques, (ii) visual representations and interaction techniques, (iii) data representations and transformations, (iv) techniques to support production, presentation, and dissemination of analytical results. The R&D agenda for visual analytics addresses technical needs for each of these focus areas, as well as recommendations for speeding the movement of promising technologies into practice. This article provides only the concise summary of the R&D agenda. We encourage reading, discussion, and debate as well as active innovation toward the agenda for visual analysis.     

Generating stable modular graphs

We present and prove the correctness of the program boundary, whose sources are available at http://people.sissa.it/~maggiolo/boundary/. Given two natural numbers g and n satisfying 2g+n−2>0, the program generates all genus g stable graphs with nunordered marked points. Each such graph determines the topological type of a nodal stable curve of arithmetic genus g with n unordered marked points. Our motivation comes from the fact that the boundary of the moduli space of stable genus g, n-pointed curves can be stratified by taking loci of curves of a fixed topological type.     

A Web-based visual analytics system for real estate data

Real estate is an important industry in most countries.However,the analysis of the real estate market is very challenging as the data are high dimensional and have complex spatial and temporal patterns.In this paper,we present a novel Web-based visual analytics system,which integrates state-of-the-art interactive visualizations to enable end users to create their own visualizations and gain insight into the real estate market.The system is implemented using the new features in HTML5,which are natively supported in current browsers.We adopt a coordinated view design in our system consisting of four major components:a map view to show the geographical information of houses,a stacked graph view to show the evolution of house sales over time,a pixel-bar view to visualize multiple attributes of houses,and a treemap view to present the hierarchical structure of the data.Novel clutter reduction methods and rich user interactions are further proposed to enhance the flexibility and analytical ability of the whole system.We have applied our system to real property market data and obtained some interesting findings.Moreover,feedback from the end users of our system is very positive.     

TabuVis: A tool for visual analytics multidimensional datasets

Efficient video summarization leads to facilely exploring video content appropriate to the user’s intention with low cognitive demand. In this paper, we present a novel approach for summarizing videos in the form of multi-scale structures that exhibit different video features at different scale levels and allow exploration of video contents with multi-scale interaction. The semantic relationship between structures is addressed and user intention is also considered and integrated in the summarization and interaction. This paper first introduces the concept of multi-scale structures for summarizing video content and describes three different types of structures that present important features at different scale levels. Furthermore, a continuous zooming interaction for browsing multi-scale structures is provided to facilitate video browsing. Finally, an elaborate user study is conducted showing that user performance on understanding and browsing videos is improved.     

A visual analytics framework for spatio-temporal analysis and modelling

To support analysis and modelling of large amounts of spatio-temporal data having the form of spatially referenced time series (TS) of numeric values, we combine interactive visual techniques with computational methods from machine learning and statistics. Clustering methods and interactive techniques are used to group TS by similarity. Statistical methods for TS modelling are then applied to representative TS derived from the groups of similar TS. The framework includes interactive visual interfaces to a library of modelling methods supporting the selection of a suitable method, adjustment of model parameters, and evaluation of the models obtained. The models can be externally stored, communicated, and used for prediction and in further computational analyses. From the visual analytics perspective, the framework suggests a way to externalize spatio-temporal patterns emerging in the mind of the analyst as a result of interactive visual analysis: the patterns are represented in the form of computer-processable and reusable models. From the statistical analysis perspective, the framework demonstrates how TS analysis and modelling can be supported by interactive visual interfaces, particularly, in a case of numerous TS that are hard to analyse individually. From the application perspective, the framework suggests a way to analyse large numbers of spatial TS with the use of well-established statistical methods for TS analysis.     

A survey of visual analytics techniques for machine learning

Computational Visual Media - Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are...     

Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics

Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.     

Defining visual languages for interactive computing

A novel definition of visual languages allows a uniform approach to satisfying the needs of visual reasoning faced in visual human-computer interaction. The way the machine associates a computational meaning with an image, and conversely, the way it generates an image on the screen from a computation are formally described. A definition of a visual sentence and of a visual language as a set of visual sentences is discussed. A hierarchy of visual languages is derived in relation to the requirements for intelligible, manageable and trustable interaction between humans and computers     

Generating random connected planar graphs

Connected planar graphs are of interest to a variety of scholars. Being able to simulate a database of such graphs with selected properties would support specific types of inference for spatial analysis and other network-based disciplines. This paper presents a simple, efficient, and flexible connected planar graph generator for this purpose. It also summarizes a comparison between an empirical set of specimen graphs and their simulated counterpart set, and establishes evidence for positing a conjecture about the principal eigenvalue of connected planar graphs. Finally, it summarizes a probability assessment of the algorithm’s outcomes as well as a comparison between the new algorithm and selected existing planar graph generators.     

Special focus on engineering visual analytics foreword

With an increasing amount of information available from complex systems, such as a software system, a medical device, and a transportation system, there is an urgent need for effective visual analytics     

An information-theoretic view of visual analytics.

Visual analytics is an emerging discipline that helps connect dots. It facilitates analytical reasoning and decision making through integrated and highly interactive visualization of complex and dynamic data and situations. Solving mysteries is only part of the game. Visual analytics must augment analyst and decision-maker capabilities to assimilate complex situations and reach informed decisions. In information theory, the information value carried by a message is the difference in information entropy before and after receipt of the message. Information entropy is a macroscopic measure of uncertainty defined on a frequency or probability distribution. The information-theoretical approach attempts to quantify discrepancies of the information content of distributions.     

An insight-based longitudinal study of visual analytics

Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term usage of the tools. A longitudinal study of a bioinformatics data set analysis is reported here. The main focus of this work is to capture the entire analysts process that an analyst goes through from a raw data set to the insights sought from the data. The study provides interesting observations about the use of visual representations and interaction mechanisms provided by the tools, and also about the process of insight generation in general. This deepens our understanding of visual analytics, guides visualization developers in creating more effective visualization tools in terms of user requirements, and guides evaluators in designing future studies that are more representative of insights sought by users from their data sets     

Generating synthetic task graphs for simulating stream computing systems

Stream-computing is an emerging computational model for performing complex operations on and across multi-source, high-volume data flows. The pool of mature publicly available applications employing this model is fairly small, and therefore the availability of workloads for various types of applications is scarce. Thus, there is a need for synthetic generation of large-scale workloads to drive simulations and estimate the performance of stream-computing applications at scale. We identify the key properties shared by most task graphs of stream-computing applications and use them to extend known random graph generation concepts with stream computing specific features, providing researchers with realistic input stream graphs. Our graph generation techniques serve the purpose of covering a disparity of potential applications and user input. Our first “domain-specific” framework exhibits high user-controlled configurability while the second “application-agnostic” framework focuses solely on emulating the key properties of general stream-computing systems, at the loss of domain-specific fine-tuning.     

GlyphLink: An interactive visualization approach for semantic graphs

Graph analysis by data visualization involves achieving a series of topology-based tasks. When the graph data belongs to a data domain that contains multiple node and link types, as in the case of semantic graphs, topology-based tasks become more challenging. To reduce visual complexity in semantic graphs, we propose an approach which is based on applying relational operations such as selecting and joining nodes of different types. We use node aggregation to reflect the relational operations to the graph. We introduce glyphs for representing aggregated nodes. Using glyphs lets us encode connectivity information of multiple nodes with a single glyph. We also use visual parameters of the glyph to encode node attributes or type specific information. Rather than doing the operations in the data abstraction layer and presenting the user with the resulting visualization, we propose an interactive approach where the user can iteratively apply the relational operations directly on the visualization. We present the efficiency of our method by the results of a usability study that includes a case study on a subset of the International Movie Database. The results of the controlled experiment in our usability study indicate a statistically significant contribution in reducing the completion time of the evaluation tasks.     

Synergistic arc-weight estimation for interactive image segmentation using graphs

We introduce a framework for synergistic arc-weight estimation, where the user draws markers inside each object (including background), arc weights are estimated from image attributes and object information (pixels under the markers), and a visual feedback guides the user’s next action. We demonstrate the method in several graph-based segmentation approaches as a basic step (which should be followed by some proper approach-specific adaptive procedure) and show its advantage over methods that do not exploit object information and over methods that recompute weights during delineation, which make the user to lose control over the segmentation process. We also validate the method using medical data from two imaging modalities (CT and MRI-T1).     

A personal visual analytics on smartphone usage data

The percentage of individuals frequently using their smartphones in work and life is increasing steadily. The interactions between individuals and their smartphones can produce large amounts of usage data, which contain rich information about smartphone owners’ usage habits and their daily life. In this paper, a personal visual analytic tool is proposed to develop insights and discover knowledge of personal life in smartphone usage data. Four cooperated visualization views and many interactions are provided in this tool to visually explore the temporal features of various interactive events between smartphones and their users, the hierarchical associations among event types, and the detailed distributions of massive event sequences. In the case study, plenty of interesting patterns are discovered by analyzing the data of two smartphone users with different usage styles. We also conduct a one-month user study on several invited volunteers from our laboratory and acquaintance circle to improve our prototype system based on their feedback.