DevLan/vispubs huggingface dataset:
Capturing and supporting the analysis process
Authors: Nazanin Kadivar, Victor Y. Chen, Dustin T. Dunsmuir, Eric Lee, Cheryl Z. Qian, John Dill, Chris Shaw 0002, Robert F. Woodbury
Published at VAST, 2009

Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw's approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.
Combining automated analysis and visualization techniques for effective exploration of high-dimensional data
Authors: Andrada Tatu, Georgia Albuquerque, Martin Eisemann, Jörn Schneidewind, Holger Theisel, Marcus A. Magnor, Daniel A. Keim
Published at VAST, 2009

Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and Parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets.
Combining iterative analytical reasoning and software development using the visualization language Processing
Authors: Claudia Müller-Birn, Lukas Birn
Published at VAST, 2009

Processing is a very powerful visualization language which combines software concepts with principles of visual form and interaction. Artists, designers and architects use it but it is also a very effective programming language in the area of visual analytics. In the following contribution Processing is utilized in order to visually analyze data provided by IEEE VAST 2009 Mini Challenge Badge and Network Traffic. The applied process is iterative and each stage of the analytical reasoning process is accompanied by customized software development. The visual model, the process and the technical solution will be briefly introduced.
Comparing two interface tools in performing visual analytics tasks
Authors: Dong Hyun Jeong, Tera Marie Green, William Ribarsky, Remco Chang
Published at VAST, 2009

In visual analytics, menu systems are commonly adopted as supporting tools because of the complex nature of data. However, it is still unknown how much the interaction implicit to the interface impacts the performance of visual analysis. To show the effectiveness of two interface tools, one a floating text-based menu (Floating Menu) and the other a more interactive iconic tool (Interactive-Icon), we evaluated the use and human performance of both tools within one highly interactive visual analytics system. We asked participants to answer similarly constructed, straightforward questions in a genomic visualization, first with one tool, and then the other. During task performance we tracked completion times, task errors, and captured coarse-grained interactive behaviors. Based on the participants accuracy, speed, behaviors and post-task qualitative feedback, we observed that although the Interactive-Icon tool supports continuous interactions, task-oriented user evaluation did not find a significant difference between the two tools because there is a familiarity effect on the performance of solving the task questions with using Floating-Menu interface tool.
Connecting the dots in visual analysis
Authors: Yedendra Babu Shrinivasan, David Gotz, Jie Lu
Published at VAST, 2009

During visual analysis, users must often connect insights discovered at various points of time. This process is often called ldquoconnecting the dots.rdquo When analysts interactively explore complex datasets over multiple sessions, they may uncover a large number of findings. As a result, it is often difficult for them to recall the past insights, views and concepts that are most relevant to their current line of inquiry. This challenge is even more difficult during collaborative analysis tasks where they need to find connections between their own discoveries and insights found by others. In this paper, we describe a context-based retrieval algorithm to identify notes, views and concepts from users' past analyses that are most relevant to a view or a note based on their line of inquiry. We then describe a related notes recommendation feature that surfaces the most relevant items to the user as they work based on this algorithm. We have implemented this recommendation feature in HARVEST, a Web based visual analytic system. We evaluate the related notes recommendation feature of HARVEST through a case study and discuss the implications of our approach.
Describing story evolution from dynamic information streams
Authors: Stuart J. Rose, Scott Butner, Wendy Cowley, Michelle L. Gregory, Julia Walker
Published at VAST, 2009

Sources of streaming information, such as news syndicates, publish information continuously. Information portals and news aggregators list the latest information from around the world enabling information consumers to easily identify events in the past 24 hours. The volume and velocity of these streams causes information from prior days to quickly vanish despite its utility in providing an informative context for interpreting new information. Few capabilities exist to support an individual attempting to identify or understand trends and changes from streaming information over time. The burden of retaining prior information and integrating with the new is left to the skills, determination, and discipline of each individual. In this paper we present a visual analytics system for linking essential content from information streams over time into dynamic stories that develop and change over multiple days. We describe particular challenges to the analysis of streaming information and present a fundamental visual representation for showing story change and evolution over time.
Detecting and analyzing relationships among anomalies
Authors: David Allen, Tsai-Ching Lu, David J. Huber
Published at VAST, 2009

The HRL anomaly analysis tool was developed as part of the IEEE VAST Challenge 2009. One of the tasks involved processing badge and network traffic in order to detect and identify a fictitious embassy employee suspected of leaking information. The tool is designed to assist an analyst in detecting, analyzing, and visualizing anomalies and their relationships. Two key visualizations in our submission present how we identified the suspicious traffic using network visualization and how subsequently we connected that activity to an employee using an alibi table.
EAKOS: VAST 2009
Authors: Lorne Leonard
Published at VAST, 2009

In this article, I describe the tools and techniques used to generate competing hypotheses for the VAST 2009 Flitter mini challenge. I will describe how I approached solving the social networks and the importance of the geospatial relationships to determine that ldquoSocial Structure Form Ardquo was the best matching social network.
Evaluating visual analytics systems for investigative analysis: Deriving design principles from a case study
Authors: Youn ah Kang, Carsten Görg, John T. Stasko
Published at VAST, 2009

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and we compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations for metrics and techniques for evaluating other visual analytics investigative analysis tools.
Finding comparable temporal categorical records: A similarity measure with an interactive visualization
Authors: Krist Wongsuphasawat, Ben Shneiderman
Published at VAST, 2009

An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity measures that capture the searcher's intent. Many similarity measures exist for numerical time series, but temporal categorical records are different. We propose a temporal categorical similarity measure, the M&amp;M (Match &amp; Mismatch) measure, which is based on the concept of aligning records by sentinel events, then matching events between the target and the compared records. The M&amp;M measure combines the time differences between pairs of events and the number of mismatches. To accom-modate customization of parameters in the M&amp;M measure and results interpretation, we implemented Similan, an interactive search and visualization tool for temporal categorical records. A usability study with 8 participants demonstrated that Similan was easy to learn and enabled them to find similar records, but users had difficulty understanding the M&amp;M measure. The usability study feedback, led to an improved version with a continuous timeline, which was tested in a pilot study with 5 participants.
finVis: Applied visual analytics for personal financial planning
Authors: Stephen Rudolph, Anya Samak, David S. Ebert
Published at VAST, 2009

FinVis is a visual analytics tool that allows the non-expert casual user to interpret the return, risk and correlation aspects of financial data and make personal finance decisions. This interactive exploratory tool helps the casual decision-maker quickly choose between various financial portfolio options and view possible outcomes. FinVis allows for exploration of inter-temporal data to analyze outcomes of short-term or long-term investment decisions. FinVis helps the user overcome cognitive limitations and understand the impact of correlation between financial instruments in order to reap the benefits of portfolio diversification. Because this software is accessible by non-expert users, decision-makers from the general population can benefit greatly from using FinVis in practical applications. We quantify the value of FinVis using experimental economics methods and find that subjects using the FinVis software make better financial portfolio decisions as compared to subjects using a tabular version with the same information. We also find that FinVis engages the user, which results in greater exploration of the dataset and increased learning as compared to a tabular display. Further, participants using FinVis reported increased confidence in financial decision-making and noted that they were likely to use this tool in practical application.
Geovisual analytics for self-organizing network data
Authors: Ho Van Quan, Tobias Åström, Mikael Jern
Published at VAST, 2009

Cellular radio networks are continually growing in both node count and complexity. It therefore becomes more difficult to manage the networks and necessary to use time and cost effective automatic algorithms to organize the networks neighbor cell relations. There have been a number of attempts to develop such automatic algorithms. Network operators, however, may not trust them because they need to have an understanding of their behavior and of their reliability and performance, which is not easily perceived. This paper presents a novel Web-enabled geovisual analytics approach to exploration and understanding of self-organizing network data related to cells and neighbor cell relations. A demonstrator and case study are presented in this paper, developed in close collaboration with the Swedish telecom company Ericsson and based on large multivariate, time-varying and geospatial data provided by the company. It allows the operators to follow, interact with and analyze the evolution of a self-organizing network and enhance their understanding of how an automatic algorithm configures locally-unique physical cell identities and organizes neighbor cell relations of the network. The geovisual analytics tool is tested with a self-organizing network that is operated by the automatic neighbor relations (ANR) algorithm. The demonstrator has been tested with positive results by a group of domain experts from Ericsson and will be tested in production.
Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates
Authors: Chad A. Steed, J. Edward Swan II, T. J. Jankun-Kelly, Patrick J. Fitzpatrick
Published at VAST, 2009

This paper demonstrates the promise of augmenting interactive multivariate representations with information from statistical processes in the domain of weather data analysis. Statistical regression, correlation analysis, and descriptive statistical calculations are integrated via graphical indicators into an enhanced parallel coordinates system, called the Multidimensional Data eXplorer (MDX). These statistical indicators, which highlight significant associations in the data, are complemented with interactive visual analysis capabilities. The resulting system allows a smooth, interactive, and highly visual workflow. The system's utility is demonstrated with an extensive hurricane climate study that was conducted by a hurricane expert. In the study, the expert used a new data set of environmental weather data, composed of 28 independent variables, to predict annual hurricane activity. MDX shows the Atlantic Meridional Mode increases the explained variance of hurricane seasonal activity by 7-15% and removes less significant variables used in earlier studies. The findings and feedback from the expert (1) validate the utility of the data set for hurricane prediction, and (2) indicate that the integration of statistical processes with interactive parallel coordinates, as implemented in MDX, addresses both deficiencies in traditional weather data analysis and exhibits some of the expected benefits of visual data analysis.
Innovative filtering techniques and customized analytics tools
Authors: Harald Bosch, Julian Heinrich, Christoph Müller 0001, Benjamin Höferlin, Guido Reina, Markus Höferlin, Michael Wörner 0001, Steffen Koch 0001
Published at VAST, 2009

The VAST 2009 Challenge consisted of three heterogeneous synthetic data sets organized into separate mini-challenges with minimal correspondence information. The challenge task was the identification of a suspected data theft from cyber and real-world traces. The grand challenge required integrating the findings from the mini challenges into a plausible, consistent scenario. A mixture of linked, customized tools based on queryable models and rapid prototyping as well as generic analysis tools (developed in-house) helped us correctly solve all of the mini challenges. A collaborative analytic process was employed to reconstruct the scenario and to propose the correct steps for the reliable identification of the criminal organization based on activity traces of its members.
Integrative visual analytics for suspicious behavior detection
Authors: Peter Bak, Christian Rohrdantz, Svenja Leifert, Christoph Granacher, Stefan Koch, Simon Butscher, Patrick Jungk, Daniel A. Keim
Published at VAST, 2009

In the VAST Challenge 2009 suspicious behavior had to be detected applying visual analytics to heterogeneous data, such as network traffic, social network enriched with geo-spatial attributes, and finally video surveillance data. This paper describes some of the awarded parts from our solution entry.
Interactive poster: A proposal for sharing user requirements for visual analytic tools
Authors: Jean Scholtz
Published at VAST, 2009

Although many in the community have advocated user-centered evaluations for visual analytic environments, a significant barrier exists. The users targeted by the visual analytics community (law enforcement personnel, professional information analysts, financial analysts, health care analysts, etc.) are often inaccessible to researchers. These analysts are extremely busy and their work environments and data are often classified or at least confidential. Furthermore, their tasks often last weeks or even months. It is simply not feasible to do such long-term observations to understand their jobs. How then can we hope to gather enough information about the diverse user populations to understand their needs? Some researchers, including the author, have been successful in getting access to specific end-users. A reasonable approach, therefore, would be to find a way to share user information. This work outlines a proposal for developing a handbook of user profiles for use by researchers, developers, and evaluators.
Interactive poster: Interactive multiobjective optimization - a new application area for visual analytics
Authors: Suvi Tarkkanen, Kaisa Miettinen, Jussi Hakanen
Published at VAST, 2009

The poster introduces interactive multiobjective optimization (IMO) as a field offering new application possibilities and challenges for visual analytics (VA), and aims at inspiring collaboration between the two fields. Our aim is to collect new ideas in order to be able to utilize VA techniques more effectively in our user interface development. Simulation-based IMO methods are developed for complex problem solving, where the expert decision maker (analyst) should be supported during the iterative process of eliciting preference information and examining the resulting output data. IMO is a subfield of multiple criteria decision making (MCDM). In simulation-based IMO, the optimization task is formulated in a mathematical model containing several conflicting objectives and constraints depending on decision variables. While using IMO methods the analyst progressively provides preference information in order to find the most satisfactory compromise between the conflicting objectives. In the poster, the implementations of two new IMO methods are used as examples to demonstrate concrete challenges of interaction design. One of them is described in this summary.
Interactive visual analysis of location reporting patterns
Authors: Derek Overby, John Keyser, Jim Wall
Published at VAST, 2009

Interactive visualization methods are often used to aid in the analysis of large datasets. We present a novel interactive visualization technique designed specifically for the analysis of location reporting patterns within large time-series datasets. We use a set of triangles with color coding to indicate the time between location reports. This allows reporting patterns (expected and unexpected) to be easily discerned during interactive analysis. We discuss the details of our method and describe evaluation both from expert opinion and from a user study.
Interactive visual clustering of large collections of trajectories
Authors: Gennady L. Andrienko, Natalia V. Andrienko, Salvatore Rinzivillo, Mirco Nanni, Dino Pedreschi, Fosca Giannotti
Published at VAST, 2009

One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multi-dimensional space of properties. However, structurally complex objects, such as trajectories of moving entities and other kinds of spatio-temporal data, cannot be adequately represented in this manner. Such data require sophisticated and computationally intensive clustering algorithms, which are very hard to scale effectively to large datasets not fitting in the computer main memory. We propose an approach to extracting meaningful clusters from large databases by combining clustering and classification, which are driven by a human analyst through an interactive visual interface.
Iterative integration of visual insights during patent search and analysis
Authors: Steffen Koch 0001, Harald Bosch, Mark Giereth, Thomas Ertl
Published at VAST, 2009

Patents are an important economic factor in todays globalized markets. Therefore, the analysis of patent information has become an inevitable task for a variety of interest groups. The retrieval of relevant patent information is an integral part of almost every patent analysis scenario. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. With `PatViz', a new system for interactive analysis of patent information has been developed to leverage iterative query refinement. PatViz supports users in building complex queries visually and in exploring patent result sets interactively. Thereby, the visual query module introduces an abstraction layer that provides uniform access to different retrieval systems and relieves users of the burden to learn different complex query languages. By establishing an integrated environment it allows for interactive reintegration of insights gained from visual result set exploration into the visual query representation. We expect that the approach we have taken is also suitable to improve iterative query refinement in other Visual Analytics systems.