DevLan/vispubs huggingface dataset:
Accelerating Network Traffic Analytics Using Query-Driven Visualization
Authors: E. Wes Bethel, Scott Campbell, Eli Dart, Kurt Stockinger, Kesheng Wu
Published at VAST, 2006

Realizing operational analytics solutions where large and complex data must be analyzed in a time-critical fashion entails integrating many different types of technology. This paper focuses on an interdisciplinary combination of scientific data management and visualization/analysis technologies targeted at reducing the time required for data filtering, querying, hypothesis testing and knowledge discovery in the domain of network connection data analysis. We show that use of compressed bitmap indexing can quickly answer queries in an interactive visual data analysis application, and compare its performance with two alternatives for serial and parallel filtering/querying on 2.5 billion records' worth of network connection data collected over a period of 42 weeks. Our approach to visual network connection data exploration centers on two primary factors: interactive ad-hoc and multiresolution query formulation and execution over n dimensions and visual display of the n-dimensional histogram results. This combination is applied in a case study to detect a distributed network scan and to then identify the set of remote hosts participating in the attack. Our approach is sufficiently general to be applied to a diverse set of data understanding problems as well as used in conjunction with a diverse set of analysis and visualization tools
Avian Flu Case Study with nSpace and GeoTime
Authors: Pascale Proulx, Sumeet Tandon, Adam Bodnar, David Schroh, Robert Harper 0002, William Wright
Published at VAST, 2006

GeoTime and nSpace are new analysis tools that provide innovative visual analytic capabilities. This paper uses an epidemiology analysis scenario to illustrate and discuss these new investigative methods and techniques. In addition, this case study is an exploration and demonstration of the analytical synergy achieved by combining GeoTime's geo-temporal analysis capabilities, with the rapid information triage, scanning and sense-making provided by nSpace. A fictional analyst works through the scenario from the initial brainstorming through to a final collaboration and report. With the efficient knowledge acquisition and insights into large amounts of documents, there is more time for the analyst to reason about the problem and imagine ways to mitigate threats. The use of both nSpace and GeoTime initiated a synergistic exchange of ideas, where hypotheses generated in either software tool could be cross-referenced, refuted, and supported by the other tool
Beyond Usability: Evaluation Aspects of Visual Analytic Environments
Authors: Jean Scholtz
Published at VAST, 2006

A new field of research, visual analytics, has been introduced. This has been defined as "the science of analytical reasoning facilitated by interactive visual interfaces" (Thomas and Cook, 2005). Visual analytic environments, therefore, support analytical reasoning using visual representations and interactions, with data representations and transformation capabilities, to support production, presentation, and dissemination. As researchers begin to develop visual analytic environments, it is advantageous to develop metrics and methodologies to help researchers measure the progress of their work and understand the impact their work has on the users who work in such environments. This paper presents five areas or aspects of visual analytic environments that should be considered as metrics and methodologies for evaluation are developed. Evaluation aspects need to include usability, but it is necessary to go beyond basic usability. The areas of situation awareness, collaboration, interaction, creativity, and utility are proposed as the five evaluation areas for initial consideration. The steps that need to be undertaken to develop systematic evaluation methodologies and metrics for visual analytic environments are outlined
Collaborative Visual Analytics: Inferring from the Spatial Organization and Collaborative Use of Information
Authors: Paul E. Keel
Published at VAST, 2006

We introduce a visual analytics environment for the support of remote-collaborative sense-making activities. Team members use their individual graphical interfaces to collect, organize and comprehend task-relevant information relative to their areas of expertise. A system of computational agents infers possible relationships among information items through the analysis of the spatial and temporal organization and collaborative use of information. The computational agents support the exchange of information among team members to converge their individual contributions. Our system allows users to navigate vast amounts of shared information effectively and remotely dispersed team members to work independently without diverting from common objectives as well as to minimize the necessary amount of verbal communication
D-Dupe: An Interactive Tool for Entity Resolution in Social Networks
Authors: Mustafa Bilgic 0001, Louis Licamele, Lise Getoor, Ben Shneiderman
Published at VAST, 2006

Visualizing and analyzing social networks is a challenging problem that has been receiving growing attention. An important first step, before analysis can begin, is ensuring that the data is accurate. A common data quality problem is that the data may inadvertently contain several distinct references to the same underlying entity; the process of reconciling these references is called entity-resolution. D-Dupe is an interactive tool that combines data mining algorithms for entity resolution with a task-specific network visualization. Users cope with complexity of cleaning large networks by focusing on a small subnetwork containing a potential duplicate pair. The subnetwork highlights relationships in the social network, making the common relationships easy to visually identify. D-Dupe users resolve ambiguities either by merging nodes or by marking them distinct. The entity resolution process is iterative: as pairs of nodes are resolved, additional duplicates may be revealed; therefore, resolution decisions are often chained together. We give examples of how users can flexibly apply sequences of actions to produce a high quality entity resolution result. We illustrate and evaluate the benefits of D-Dupe on three bibliographic collections. Two of the datasets had already been cleaned, and therefore should not have contained duplicates; despite this fact, many duplicates were rapidly identified using D-Dupe's unique combination of entity resolution algorithms within a task-specific visual interface
Enhancing Visual Analysis of Network Traffic Using a Knowledge Representation
Authors: Ling Xiao, John Gerth, Pat Hanrahan
Published at VAST, 2006

This paper presents a network traffic analysis system that couples visual analysis with a declarative knowledge representation. The system supports multiple iterations of the sense-making loop of analytic reasoning by allowing users to save discoveries as they are found and to reuse them in future iterations. We show how the knowledge representation can be used to improve both the visual representations and the basic analytical tasks of filtering and changing level of detail. We describe how the system can be used to produce models of network patterns, and show results from classifying one day of network traffic in our laboratory
Exploratory Visualization of Multivariate Data with Variable Quality
Authors: Zaixian Xie, Shiping Huang, Matthew O. Ward, Elke A. Rundensteiner
Published at VAST, 2006

Real-world data is known to be imperfect, suffering from various forms of defects such as sensor variability, estimation errors, uncertainty, human errors in data entry, and gaps in data gathering. Analysis conducted on variable quality data can lead to inaccurate or incorrect results. An effective visualization system must make users aware of the quality of their data by explicitly conveying not only the actual data content, but also its quality attributes. While some research has been conducted on visualizing uncertainty in spatio-temporal data and univariate data, little work has been reported on extending this capability into multivariate data visualization. In this paper we describe our approach to the problem of visually exploring multivariate data with variable quality. As a foundation, we propose a general approach to defining quality measures for tabular data, in which data may experience quality problems at three granularities: individual data values, complete records, and specific dimensions. We then present two approaches to visual mapping of quality information into display space. In particular, one solution embeds the quality measures as explicit values into the original dataset by regarding value quality and record quality as new data dimensions. The other solution is to superimpose the quality information within the data visualizations using additional visual variables. We also report on user studies conducted to assess alternate mappings of quality attributes to visual variables for the second method. In addition, we describe case studies that expose some of the advantages and disadvantages of these two approaches
Exploring Large-Scale Video News via Interactive Visualization
Authors: Hangzai Luo, Jianping Fan 0001, Jing Yang 0001, William Ribarsky, Shin'ichi Satoh 0001
Published at VAST, 2006

In this paper, we have developed a novel visualization framework to enable more effective visual analysis of large-scale news videos, where keyframes and keywords are automatically extracted from news video clips and visually represented according to their interestingness measurement to help audiences rind news stories of interest at first glance. A computational approach is also developed to quantify the interestingness measurement of video clips. Our experimental results have shown that our techniques for intelligent news video analysis have the capacity to enable more effective visualization of large-scale news videos. Our news video visualization system is very useful for security applications and for general audiences to quickly find news topics of interest from among many channels
Have Green - A Visual Analytics Framework for Large Semantic Graphs
Authors: Pak Chung Wong, George Chin, Harlan Foote, Patrick Mackey, James J. Thomas
Published at VAST, 2006

A semantic graph is a network of heterogeneous nodes and links annotated with a domain ontology. In intelligence analysis, investigators use semantic graphs to organize concepts and relationships as graph nodes and links in hopes of discovering key trends, patterns, and insights. However, as new information continues to arrive from a multitude of sources, the size and complexity of the semantic graphs will soon overwhelm an investigator's cognitive capacity to carry out significant analyses. We introduce a powerful visual analytics framework designed to enhance investigators' natural analytical capabilities to comprehend and analyze large semantic graphs. The paper describes the overall framework design, presents major development accomplishments to date, and discusses future directions of a new visual analytics system known as Have Green
Interactive Visual Synthesis of Analytic Knowledge
Authors: David Gotz, Michelle X. Zhou, Vikram Aggarwal
Published at VAST, 2006

A visual investigation involves both the examination of existing information and the synthesis of new analytic knowledge. This is a progressive process in which newly synthesized knowledge becomes the foundation for future discovery. In this paper, we present a novel system supporting interactive, progressive synthesis of analytic knowledge. Here we use the term "analytic knowledge" to refer to concepts that a user derives from existing data along with the evidence supporting such concepts. Unlike existing visual analytic-tools, which typically support only exploration of existing information, our system offers two unique features. First, we support user-system cooperative visual synthesis of analytic knowledge from existing data. Specifically, users can visually define new concepts by annotating existing information, and refine partially formed concepts by linking additional evidence or manipulating related concepts. In response to user actions, our system can automatically manage the evolving corpus of synthesized knowledge and its corresponding evidence. Second, we support progressive visual analysis of synthesized knowledge. This feature allows analysts to visually explore both existing knowledge and synthesized knowledge, dynamically incorporating earlier analytic conclusions into the ensuing discovery process. We have applied our system to two complex but very different analytic applications. Our preliminary evaluation shows the promise of our work
Interactive Visualization and Analysis of Network and Sensor Data on Mobile Devices
Authors: Avin Pattath, Brian D. Bue, Yun Jang, David S. Ebert, Xuan Zhong, Aaron Ault, Edward J. Coyle
Published at VAST, 2006

Mobile devices are rapidly gaining popularity due to their small size and their wide range of functionality. With the constant improvement in wireless network access, they are an attractive option not only for day to day use. but also for in-field analytics by first responders in widespread areas. However, their limited processing, display, graphics and power resources pose a major challenge in developing effective applications. Nevertheless, they are vital for rapid decision making in emergencies when combined with appropriate analysis tools. In this paper, we present an efficient, interactive visual analytic system using a PDA to visualize network information from Purdue's Ross-Ade Stadium during football games as an example of in-held data analytics combined with text and video analysis. With our system, we can monitor the distribution of attendees with mobile devices throughout the stadium through their access of information and association/disassociation from wireless access points, enabling the detection of crowd movement and event activity. Through correlative visualization and analysis of synchronized video (instant replay video) and text information (play statistics) with the network activity, we can provide insightful information to network monitoring personnel, safety personnel and analysts. This work provides a demonstration and testbed for mobile sensor analytics that will help to improve network performance and provide safety personnel with information for better emergency planning and guidance
Interactive Wormhole Detection in Large Scale Wireless Networks
Authors: Weichao Wang, Aidong Lu
Published at VAST, 2006

Wormhole attacks in wireless networks can severely deteriorate the network performance and compromise the security through spoiling the routing protocols and weakening the security enhancements. This paper develops an approach, interactive visualization of wormholes (IVoW), to monitor and detect such attacks in large scale wireless networks in real time. We characterize the topology features of a network under wormhole attacks through the node position changes and visualize the information at dynamically adjusted scales. We integrate an automatic detection algorithm with appropriate user interactions to handle complicated scenarios that include a large number of moving nodes and multiple worm-hole attackers. Various visual forms have been adopted to assist the understanding and analysis of the reconstructed network topology and improve the detection accuracy. Extended simulation has demonstrated that the proposed approach can effectively locate the fake neighbor connections without introducing many false alarms. IVoW does not require the wireless nodes to be equipped with any special hardware, thus avoiding any additional cost. The proposed approach demonstrates that interactive visualization can be successfully combined with network security mechanisms to greatly improve the intrusion detection capabilities
Monitoring Network Traffic with Radial Traffic Analyzer
Authors: Daniel A. Keim, Florian Mansmann, Jörn Schneidewind, Tobias Schreck
Published at VAST, 2006

Extensive spread of malicious code on the Internet and also within intranets has risen the user's concern about what kind of data is transferred between her or his computer and other hosts on the network. Visual analysis of this kind of information is a challenging task, due to the complexity and volume of the data type considered, and requires special design of appropriate visualization techniques. In this paper, we present a scalable visualization toolkit for analyzing network activity of computer hosts on a network. The visualization combines network packet volume and type distribution information with geographic information, enabling the analyst to use geographic distortion techniques such as the HistoMap technique to become aware of the traffic components in the course of the analysis. The presented analysis tool is especially useful to compare important network load characteristics in a geographically aware display, to relate communication partners, and to identify the type of network traffic occurring. The results of the analysis are helpful in understanding typical network communication activities, and in anticipating potential performance bottlenecks or problems. It is suited for both off-line analysis of historic data, and via animation for on-line monitoring of packet-based network traffic in real time
NetLens: Iterative Exploration of Content-Actor Network Data
Authors: Hyunmo Kang, Catherine Plaisant, Bongshin Lee, Benjamin B. Bederson
Published at VAST, 2006

Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper offers an abstract content-actor network data model, a classification of tasks, and a tool to support them. The NetLens interface was designed around the abstract content-actor network data model to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify. NetLens is general and scalable in that it applies to any dataset that can be represented with our abstract data model. This paper describes NetLens applying a subset of the ACM Digital Library consisting of about 4,000 papers from the CM I conference written by about 6,000 authors. In addition, we are now working on a collection of half a million emails, and a dataset of legal cases
Pixnostics: Towards Measuring the Value of Visualization
Authors: Jörn Schneidewind, Mike Sips, Daniel A. Keim
Published at VAST, 2006

During the last two decades a wide variety of advanced methods for the visual exploration of large data sets have been proposed. For most of these techniques user interaction has become a crucial element, since there are many situations in which a user or an analyst has to select the right parameter settings from among many or select a subset of the available attribute space for the visualization process, in order to construct valuable visualizations that provide insight, into the data and reveal interesting patterns. The right choice of input parameters is often essential, since suboptimal parameter settings or the investigation of irrelevant data dimensions make the exploration process more time consuming and may result in wrong conclusions. In this paper we propose a novel method for automatically determining meaningful parameter- and attribute settings based on the information content of the resulting visualizations. Our technique called Pixnostics, in analogy to Scagnostics (Wilkinson et al., 2005), automatically analyses pixel images resulting from diverse parameter mappings and ranks them according to the potential value for the user. This allows a more effective and more efficient visual data analysis process, since the attribute/parameter space is reduced to meaningful selections and thus the analyst obtains faster insight into the data. Real world applications are provided to show the benefit of the proposed approach
Scentindex: Conceptually Reorganizing Subject Indexes for Reading
Authors: Ed H. Chi, Lichan Hong, Julie Heiser, Stuart K. Card
Published at VAST, 2006

A great deal of analytical work is done in the context of reading, in digesting the semantics of the material, the identification of important entities, and capturing the relationship between entities. Visual analytic environments, therefore, must encompass reading tools that enable the rapid digestion of large amount of reading material. Other than plain text search, subject indexes, and basic highlighting, tools are needed for rapid foraging of text. In this paper, we describe a technique that presents an enhanced subject index for a book by conceptually reorganizing it to suit particular expressed user information needs. Users first enter information needs via keywords describing the concepts they are trying to retrieve and comprehend. Then our system, called ScentIndex, computes what index entries are conceptually related and reorganizes and displays these index entries on a single page. We also provide a number of navigational cues to help users peruse over this list of index entries and find relevant passages quickly. Compared to regular reading of a paper book, our study showed that users are more efficient and more accurate in finding, comparing, and comprehending material in our system
Semantic Image Browser: Bridging Information Visualization with Automated Intelligent Image Analysis
Authors: Jing Yang 0001, Jianping Fan 0001, Daniel Hubball, Yuli Gao, Hangzai Luo, William Ribarsky, Matthew O. Ward
Published at VAST, 2006

Browsing and retrieving images from large image collections are becoming common and important activities. Semantic image analysis techniques, which automatically detect high level semantic contents of images for annotation, are promising solutions toward this problem. However, few efforts have been made to convey the annotation results to users in an intuitive manner to enable effective image browsing and retrieval. There is also a lack of methods to monitor and evaluate the automatic image analysis algorithms due to the high dimensional nature of image data, features, and contents. In this paper, we propose a novel, scalable semantic image browser by applying existing information visualization techniques to semantic image analysis. This browser not only allows users to effectively browse and search in large image databases according to the semantic content of images, but also allows analysts to evaluate their annotation process through interactive visual exploration. The major visualization components of this browser are multi-dimensional scaling (MDS) based image layout, the value and relation (VaR) display that allows effective high dimensional visualization without dimension reduction, and a rich set of interaction tools such as search by sample images and content relationship detection. Our preliminary user study showed that the browser was easy to use and understand, and effective in supporting image browsing and retrieval tasks