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Visual Analytics Science And Technology, 2006 IEEE Symposium On

Date Oct. 31 2006-Nov. 2 2006

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Displaying Results 1 - 25 of 38
  • [Front cover]

    Page(s): C1
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  • [Title page]

    Page(s): C2
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  • Author index

    Page(s): ii
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  • IEEE Symposium on Visual Analytics Science and Technology 2006

    Page(s): iii
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  • Copyright page

    Page(s): iv
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  • Message from the Symposium and Paper Chairs

    Page(s): v
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  • IEEE Visualization and Graphics Technical Committee (VGTC)

    Page(s): vi
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  • Conference Committee

    Page(s): vii
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  • Paper reviewers

    Page(s): viii
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  • Designer Information - Why Visualization and Analytics Technologies Should Help Us Focus Our Minds and Not Our Senses

    Page(s): ix
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  • Time Tree: Exploring Time Changing Hierarchies

    Page(s): 3 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1080 KB) |  | HTML iconHTML  

    Intelligence analysis often involves the task of gathering information about an organization. Knowledge about individuals in an organization and their relationships, often represented as a hierarchical organization chart, is crucial for understanding the organization. However, it is difficult for intelligence analysts to follow all individuals in an organization. Existing hierarchy visualizations have largely focused on the visualization of fixed structures and can not effectively depict the evolution of a hierarchy over time. We introduce TimeTree, a novel visualization tool designed to enable exploration of a changing hierarchy. TimeTree enables analysts to navigate the history of an organization, identify events associated with a specific entity (visualized on a TimeSlider), and explore an aggregate view of an individual's career path (a CareerTree). We demonstrate the utility of TimeTree by investigating a set of scenarios developed by an expert intelligence analyst. The scenarios are evaluated using a real dataset composed of eighteen thousand career events from more than eight thousand individuals. Insights gained from this analysis are presented View full abstract»

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  • Visual Exploration of Spatio-temporal Relationships for Scientific Data

    Page(s): 11 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (653 KB) |  | HTML iconHTML  

    Spatio-temporal relationships among features extracted from temporally-varying scientific datasets can provide useful information about the evolution of an individual feature and its interactions with other features. However, extracting such useful relationships without user guidance is cumbersome and often an error prone process. In this paper, we present a visual analysis system that interactively discovers such relationships from the trajectories of derived features. We describe analysis algorithms to derive various spatial and spatio-temporal relationships. A visual interface is presented using which the user can interactively select spatial and temporal extents to guide the knowledge discovery process. We show the usefulness of our proposed algorithms on datasets originating from computational fluid dynamics. We also demonstrate how the derived relationships can help in explaining the occurrence of critical events like merging and bifurcation of the vortices View full abstract»

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  • Visual Analytics of Paleoceanographic Conditions

    Page(s): 19 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (33020 KB) |  | HTML iconHTML  

    Decade scale oceanic phenomena like El Nino are correlated with weather anomalies all over the globe. Only by understanding the events that produced the climatic conditions in the past will it be possible to forecast abrupt climate changes and prevent disastrous consequences for human beings and their environment. Paleoceanography research is a collaborative effort that requires the analysis of paleo time-series, which are obtained from a number of independent techniques and instruments and produced by a variety of different researchers and/or laboratories. The complexity of these phenomena that consist of massive, dynamic and often conflicting data can only be faced by means of analytical reasoning supported by a highly interactive visual interface. This paper presents an interactive visual analysis environment for paleoceanography that permits to gain insight into the paleodata and allow the control and steering of the analytical methods involved in the reconstruction of the climatic conditions of the past View full abstract»

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  • Avian Flu Case Study with nSpace and GeoTime

    Page(s): 27 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1636 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Visual Analysis of Historic Hotel Visitation Patterns

    Page(s): 35 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1701 KB) |  | HTML iconHTML  

    Understanding the space and time characteristics of human interaction in complex social networks is a critical component of visual tools for intelligence analysis, consumer behavior analysis, and human geography. Visual identification and comparison of patterns of recurring events is an essential feature of such tools. In this paper, we describe a tool for exploring hotel visitation patterns in and around Rebersburg, Pennsylvania from 1898-1900. The tool uses a wrapping spreadsheet technique, called reruns, to display cyclic patterns of geographic events in multiple overlapping natural and artificial calendars. Implemented as an improvise visualization, the tool is in active development through a iterative process of data collection, hypothesis, design, discovery, and evaluation in close collaboration with historical geographers. Several discoveries have inspired ongoing data collection and plans to expand exploration to include historic weather records and railroad schedules. Distributed online evaluations of usability and usefulness have resulted in numerous feature and design recommendations View full abstract»

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  • D-Dupe: An Interactive Tool for Entity Resolution in Social Networks

    Page(s): 43 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (689 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Interactive Visual Synthesis of Analytic Knowledge

    Page(s): 51 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1965 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Visual Analysis of Conflicting Opinions

    Page(s): 59 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2399 KB)  

    Understanding the nature and dynamics of conflicting opinions is a profound and challenging issue. In this paper we address several aspects of the issue through a study of more than 3,000 Amazon customer reviews of the controversial bestseller The Da Vinci Code, including 1,738 positive and 918 negative reviews. The study is motivated by critical questions such as: what are the differences between positive and negative reviews? What is the origin of a particular opinion? How do these opinions change over time? To what extent can differentiating features be identified from unstructured text? How accurately can these features predict the category of a review? We first analyze terminology variations in these reviews in terms of syntactic, semantic, and statistic associations identified by TermWatch and use term variation patterns to depict underlying topics. We then select the most predictive terms based on log likelihood tests and demonstrate that this small set of terms classifies over 70% of the conflicting reviews correctly. This feature selection process reduces the dimensionality of the feature space from more than 20,000 dimensions to a couple of hundreds. We utilize automatically generated decision trees to facilitate the understanding of conflicting opinions in terms of these highly predictive terms. This study also uses a number of visualization and modeling tools to identify not only what positive and negative reviews have in common, but also they differ and evolve over time View full abstract»

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  • Have Green - A Visual Analytics Framework for Large Semantic Graphs

    Page(s): 67 - 74
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3814 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Exploring Large-Scale Video News via Interactive Visualization

    Page(s): 75 - 82
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (808 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Interactive Visualization and Analysis of Network and Sensor Data on Mobile Devices

    Page(s): 83 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3986 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • NetLens: Iterative Exploration of Content-Actor Network Data

    Page(s): 91 - 98
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (37077 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Interactive Wormhole Detection in Large Scale Wireless Networks

    Page(s): 99 - 106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3341 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Enhancing Visual Analysis of Network Traffic Using a Knowledge Representation

    Page(s): 107 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (988 KB) |  | HTML iconHTML  

    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 View full abstract»

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  • Accelerating Network Traffic Analytics Using Query-Driven Visualization

    Page(s): 115 - 122
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (201 KB) |  | HTML iconHTML  

    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 View full abstract»

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