Eye movements play an important role during social interaction, for instance, by indicating phases of mutual and joint attention. This type of gaze behavior has been researched extensively in pair collaboration but has been far less studied in the investigation of group activities. Further, the analysis of attention is often decoupled from speech, even though they are closely linked in social interactions. We introduce a visualization to facilitate joint analysis of gaze and speech among multiple participants during social activities. Our proposed social interaction graphs are two-layered arc diagrams that visualize pairwise relationships among participants. Our evaluation is based on two datasets of multiplayer tabletop gaming sessions recorded with five players. We present a traditional evaluation using eye-tracking metrics as a baseline and compare it with the additional findings possible with our approach.

Abstract Case studies are central to evaluating visualization research as they provide evidence for how the developed approaches support real users, real analytical work, and real data. Conducting such studies can be challenging, since target users with relevant domain expertise are often scarce or even entirely unavailable, while the visualization researchers may lack the required expertise to perform the evaluation themselves. Current research on Large Language Models (LLMs) has shown their strong reasoning ability and usefulness in domain‐specific downstream tasks, provoking the question: to what degree and with what capacity can LLMs fill the role of the target users in visualization case studies? We investigate this question by evaluating how LLMs can participate across different phases of visualization case studies. We propose a conceptual framework that explores the different levels of LLM involvement and potential roles when simulating target users. To sketch where the LLM substitution is most plausible, we embed our suggested integration of LLMs within the nested model for visualization theory and design. For evaluation, we replicate case studies from published and unpublished visualization research using multiple state‐of‐the‐art LLMs and compare the model‐generated insights to those reported in the papers. Our results show that LLMs can often generate plausible and sometimes even novel interpretations of visual patterns when used for result analysis and validation, but can also struggle when highly contextual, domain‐specific knowledge is required.

In digital humanities (DH) and cultural heritage (CH), visualization-based storytelling (VBS) has become an important approach for structuring, interpreting, and communicating cultural data and research results. The specific characteristics of these domains – historically oriented data, high levels of semantic ambiguity and uncertainty, interpretive rather than purely analytical goals, and deep expertise in narrative theory and practice – create both distinct requirements for VBS design and distinct opportunities that existing frameworks and tools only partially address. Yet the body of work that has emerged in response to these requirements remains scattered across disciplines and venues, with no systematic account of current practice and no consolidated view of open challenges.Against this backdrop, we survey DH and CH work on story designs and VBS tools to identify trends and recurring patterns, promising practices, and open challenges. We contribute by (1) synthesizing storytelling design spaces into a framework tailored to VBS in DH and CH, (2) mapping existing approaches to generate a field-level picture of practices and gaps, and (3) highlighting future areas of concern and inquiry for VBS in relation to domain-specific epistemic questions. Overall, this survey seeks to consolidate an emerging community of practice and offer a shared analytical foundation for future research and design.

Image-generative models have gained popularity over the last years with their ability to create realistic artwork. Realizing complex artworks with specific creative ideas often requires iterative optimization of specialized prompts, but may still result in inadequate images. The inclusion of reference images and adapting modelspecific parameters can help in steering the model and fostering the creative intent of the user. But by providing text prompts, initial images, and adapting model parameters, users face a vast design space for creating images. To navigate through this space, we propose a visualization approach that combines an interactive Provenance Graph, parameter visualizations, and high-dimensional embeddings. Our approach helps pursue multiple parallel creation paths, makes workflows traceable and parameter changes transparent, and facilitates the reporting of image editing steps. In addition to prompt formulation, we focus on targeted generation by probing parameters, image compositions, and editing details. We integrate the generative process into existing image editing software, enabling users to compose artwork in collaboration with the model. The presented approach is evaluated in a user experiment ($\mathrm{n}=9$) for generating artwork. The results show that users with different levels of experience can create targeted artwork but use different strategies when working with the Provenance Graph.

Writing and editing documents is often an iterative process. Understanding the evolution of document editing can help improve and shape future writing projects. Visualizing the document editing process can assist in this, by providing a global overview of the process, as well as enabling detailed comparison between document versions. We propose an approach for analyzing iterative writing and editing processes visually. To support the understanding of these processes, we base our interactive visualization approach on the layout of the document and provide multiple views focusing on different aspects of the document evolution. Our approach is text token agnostic to increase its generalizability. We exemplify its usage with the process of scientific writing, applying it to one of our own paper writing projects.

This January 2025 issue of the IEEE Transactions on Visualization and Computer Graphics (TVCG) contains the proceedings of IEEE VIS 2024, held on October 1318 October, 2024 in St. Pete Beach, Florida, USA, with the three General Chairs Paul Rosen (University of Utah), Kristi Potter (U.S. National Renewable Energy Laboratory), and Remco Chang (Tufts University). With IEEE VIS 2024, the conference series is in its 35th year.

Large transparent touch screens (LTTS) have recently become commercially available. These displays have the potential for engaging Augmented Reality (AR) applications, especially in public and shared spaces. However, the interaction with objects in the real environment behind the display remains challenging: Users must combine pointing and touch input if they want to select objects at varying distances. There is a lot of work on wearable or mobile AR displays, but little on how users interact with LTTS. Our goal is to contribute to a better understanding of natural user interaction for these AR displays. To this end, we developed a prototype and evaluated different pointing techniques for selecting 12 physical targets behind an LTTS, with distances ranging from 6 to 401 cm. We conducted a user study with 16 participants and measured user preferences, performance, and behavior. We analyzed the change in accuracy depending on the target position and the selection technique used. Our findings include: (a) Users naturally align the touch point with their line of sight for targets farther than 36 cm behind the LTTS. (b) This technique provides the lowest angular deviation compared to other techniques. (c) Some user close one eye to improve their performance. Our results help to improve future AR scenarios using LTTS systems.

Despite the availability of hotel booking platforms with customer reviews, selecting the best hotel or accommodation can still be difficult. This is partly because it is hard to directly contrast multiple shortlisted hotels. We propose a new visual interface, called ViSCitR, that goes beyond existing solutions in visually comparing ratings and personalizing the evaluation based on individual priorities. Targeted at a broad audience, it integrates several dimensions of comparison into a highly interactive document. The document contrasts the hotels visually and textually from different perspectives, including (i) a geographic perspective with points of interest, (ii) a comparison of ratings across different categories, (iii) rating changes over time, and (iv) summaries of the most noted positive and negative points from the review texts. The interface is accompanied by a sidebar for personalization, which supports selecting relevant points of interest and setting priorities. Changing these adapts the visual comparison across all interface sections to match the user’s preferences. A qualitative evaluation with potential hotel customers showed that the personalized visual comparison is valuable for all users, while the advanced features of the interface are leveraged to different extents.

The visualization and interactive exploration of geo-referenced networks poses challenges if the network’s nodes are not evenly distributed. Our approach proposes new ways of realizing animated transitions for exploring such networks from an ego-perspective. We aim to reduce the required screen estate while maintaining the viewers’ mental map of distances and directions. A preliminary study provides first insights of the comprehensiveness of animated geographic transitions regarding directional relationships between start and end point in different projections. Two use cases showcase how ego-perspective graph exploration can be supported using less screen space than previous approaches.

Abstract Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real‐world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.

Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one annotated frame per action. However, it often suffers from poor performance due to the lack of precise boundary annotations. To address this issue, we propose a visual analysis method that aligns similar actions and then propagates a few user-provided annotations (e.g., boundaries, category labels) to similar actions via the generated alignments. Our method models the alignment between actions as a heaviest path problem and the annotation propagation as a quadratic optimization problem. As the automatically generated alignments may not accurately match the associated actions and could produce inaccurate localization results, we develop a storyline visualization to explain the localization results of actions and their alignments. This visualization facilitates users in correcting wrong localization results and misalignments. The corrections are then used to improve the localization results of other actions. The effectiveness of our method in improving localization performance is demonstrated through quantitative evaluation and a case study.

In visualization research, reproducibility is often not taken into account as a design goal. Reproducibility is desirable beyond ensuring scientific rigor: In the long-term, it helps advance sustainable visualization research, and in the short-term, it supports domain experts using the visualizations in their daily work. Designing for and ensuring reproducibility introduces costs, such as as storage cost or infrastructure maintenance. We propose a typology of reproducibility aspects that can be considered during visualization design. We then propose practical strategies to improve reproducibility and discuss their initial and running costs, their limits, and their tradeoffs. Finally, we discuss three concrete visualization approaches in the context of our typology and strategies.

We present a new hierarchical multifocus representation- and interaction-technique for the analysis of text annotations. The comparative analysis of annotation data on multiple, distant passages (focus regions) of a long source text raises several scalability problems. In particular, one soon can be confronted with many nested foci on text ranges with sizes of different orders of magnitude. Our solution suggests to delegate the detailed data representation to other views and to concentrate in the presented overview on the organization of the focus regions. The approach consists of three parts: a collection of strips where the annotations are represented in a condensed form and where sibling- and child-foci are manipulated, a map of the resulting tree of foci for direct access, and a work bench that allows to compare deliberate nodes of the tree on a flat layer. We discuss our approach by comparing it with other state-of-the-art multifocus techniques and describe two use cases that deal with relational annotation and with the analysis of annotations on hierarchical text structures respectively.

Test methods that can keep up with the ongoing increase in complexity of semiconductor products and their underlying technologies are an essential prerequisite for maintaining quality and safety of our daily lives and for continued success of our economies and societies. There is a huge potential how test methods can benefit from recent breakthroughs in domains such as artificial intelligence, data analytics, virtual/augmented reality, and security. The Graduate School on “Intelligent Methods for Semiconductor Test and Reliability” (GS-IMTR) at the University of Stuttgart is a large-scale, radically interdisciplinary effort to address the scientific-technological challenges in this domain. It is funded by Advantest, one of the world leaders in automatic test equipment. In this paper, we describe the overall philosophy of the Graduate School and the specific scientific questions targeted by its ten projects.

Traffic analysis in large enterprise networks has become a vital task for network experts, as understanding application and user traffic is the basis for proper network management with respect to planning, formulating intents, or analyzing causes for implausible behavior. In such networks, NetFlow provides input to network monitoring systems that typically show time series visualizations along different data dimensions. We studied tasks and requirements of network experts and derived a visual analytics approach that improves their analytic workflow as it enables for exploration of large time spans quickly in a multidimensional manner. Our approach guides users and improves the scalability of analyses through a novel combination of a clustered time series view and filtering in interactive parallel sets into a coherent visual analysis framework. Clustering reveals typical patterns and deviations from the daily norm and serves as entry point to exploring, filtering and comparing multiple dimensions in the parallel sets view. In addition, we briefly discuss the feedback we received on two case studies with network experts.

When visualizations are used to augment text, geographical information on a map, or on mobile devices, there often is not enough space to show complex data with approaches like juxtaposed visualizations or coordinated views. To alleviate this issue, we propose the use of animated transitions between several small-scale visualizations. We discuss design considerations for animated transitions in small-scale visualizations. We further present the results of a study on the effectiveness of those animated transitions in conveying information and attribute relations, and the mental load of following the animated transitions.

Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.

Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the suitability of the algorithm for larger values of k depending on the size of the collection. Optimizations targeted at the Euclidean k-Means algorithm largely do not apply because the cosine distance is not a metric. We therefore propose an efficient indexing structure to improve the scalability of Spherical k-Means with respect to k. Our approach exploits the sparsity of the input vectors and the convergence behavior of k-Means to reduce the number of comparisons on each iteration significantly.

Keyphrase extraction methods can provide insights into large collections of documents such as social media posts. Existing methods, however, are less suited for the real-time analysis of streaming data, because they are computationally too expensive or require restrictive constraints regarding the structure of keyphrases. We propose an efficient approach to extract keyphrases from large document collections and show that the method also performs competitively on individual documents.

Abstract It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio‐temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio‐temporal data, based on a set of neighborhood‐preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real‐world application scenarios and discuss the feedback we received from domain and visualization experts.

It is difficult to explore large text collections if no or little information is available on the contained documents. Hence, starting analytic tasks on such corpora is challenging for many stakeholders from various domains. As a remedy, recent visualization research suggests to use visual spatializations of representative text documents or tags to explore text collections. With PyramidTags, we introduce a novel approach for summarizing large text collections visually. In contrast to previous work, PyramidTags in particular aims at creating an improved representation that incorporates both temporal evolution and semantic relationship of visualized tags within the summarized document collection. As a result, it equips analysts with a visual starting point for interactive exploration to not only get an overview of the main terms and phrases of the corpus, but also to grasp important ideas and stories. Analysts can hover and select multiple tags to explore relationships and retrieve the most relevant documents. In this work, we apply PyramidTags to hundreds of thousands of web-crawled news reports. Our benchmarks suggest that PyramidTags creates time- and context-aware layouts, while preserving the inherent word order of important pairs.

Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.

Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.

Magic lens based focus+context techniques are powerful means for exploring document spatializations. Typically, they only offer additional summarized or abstracted views on focused documents. As a consequence, users might miss important information that is either not shown in aggregated form or that never happens to get focused. In this work, we present the design process and user study results for improving a magic lens based document exploration approach with exemplary visual quality cues to guide users in steering the exploration and support them in interpreting the summarization results. We contribute a thorough analysis of potential sources of information loss involved in these techniques, which include the visual spatialization of text documents, user-steered exploration, and the visual summarization. With lessons learned from previous research, we highlight the various ways those information losses could hamper the exploration. Furthermore, we formally define measures for the aforementioned different types of information losses and bias. Finally, we present the visual cues to depict these quality measures that are seamlessly integrated into the exploration approach. These visual cues guide users during the exploration and reduce the risk of misinterpretation and accelerate insight generation. We conclude with the results of a controlled user study and discuss the benefits and challenges of integrating quality guidance in exploration techniques.

Magic lens based focus+context techniques are powerful means for exploring document spatializations. Typically, they only offer additional summarized or abstracted views on focused documents. As a consequence, users might miss important information that is either not shown in aggregated form or that never happens to get focused. In this work, we present the design process and user study results for improving a magic lens based document exploration approach with exemplary visual quality cues to guide users in steering the exploration and support them in interpreting the summarization results. We contribute a thorough analysis of potential sources of information loss involved in these techniques, which include the visual spatialization of text documents, user-steered exploration, and the visual summarization. With lessons learned from previous research, we highlight the various ways those information losses could hamper the exploration. Furthermore, we formally define measures for the aforementioned different types of information losses and bias. Finally, we present the visual cues to depict these quality measures that are seamlessly integrated into the exploration approach. These visual cues guide users during the exploration and reduce the risk of misinterpretation and accelerate insight generation. We conclude with the results of a controlled user study and discuss the benefits and challenges of integrating quality guidance in exploration techniques.

editorial Free Access Share on ACM TIST Special Issue on Visual Analytics Authors: Nan Cao Tongji University Tongji UniversityView Profile , Steffen Koch University of Stuttgart University of StuttgartView Profile , David Gotz University of North Carolina at Chapel Hill University of North Carolina at Chapel HillView Profile , Editor: Yingcai Wu State Key Lab of CAD8CG Zhejiang University State Key Lab of CAD8CG Zhejiang UniversityView Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 10Issue 1January 2019 Article No.: 1pp 1–4https://doi.org/10.1145/3277019Published:13 December 2018Publication History 0citation404DownloadsMetricsTotal Citations0Total Downloads404Last 12 Months43Last 6 weeks10 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 SiteView all FormatsPDF

The increasingly large number of available writings describing technical and scientific progress, calls for advanced analytic tools for their efficient analysis. This is true for many application scenarios in science and industry and for different types of writings, comprising patents and scientific articles. Despite important differences between patents and scientific articles, both have a variety of common characteristics that lead to similar search and analysis tasks. However, the analysis and visualization of these documents is not a trivial task due to the complexity of the documents as well as the large number of possible relations between their multivariate attributes. In this survey, we review interactive analysis and visualization approaches of patents and scientific articles, ranging from exploration tools to sophisticated mining methods. In a bottom-up approach, we categorize them according to two aspects: (a) data type (text, citations, authors, metadata, and combinations thereof), and (b) task (finding and comparing single entities, seeking elementary relations, finding complex patterns, and in particular temporal patterns, and investigating connections between multiple behaviours). Finally, we identify challenges and research directions in this area that ask for future investigations.

We have created and made available to all a dataset with information about every paper that has appeared at the IEEE Visualization (VIS) set of conferences: InfoVis, SciVis, VAST, and Vis. The information about each paper includes its title, abstract, authors, and citations to other papers in the conference series, among many other attributes. This article describes the motivation for creating the dataset, as well as our process of coalescing and cleaning the data, and a set of three visualizations we created to facilitate exploration of the data. This data is meant to be useful to the broad data visualization community to help understand the evolution of the field and as an example document collection for text data visualization research.

In information visualization, evaluation plays a crucial role during the development of a new visualization technique. In recent years, eye tracking has become one means to analyze how users perceive and understand a new visualization system. Since most visualizations are highly interactive nowadays, a study should take interaction, in terms of user-input, into account as well. In addition, think aloud data gives insights into cognitive processes of participants using a visualization system. Typically, researchers evaluate these data sources separately. However, we think it is beneficial to correlate eye tracking, interaction, and think aloud data for deeper analyses. In this paper, we present challenges and possible solutions in triangulating user behavior using multiple evaluation data sources. We describe how the data is collected, synchronized, and analyzed using a string-based and a visualization-based approach founded on experiences from our current research. We suggest methods how to tackle these issues and discuss benefits and disadvantages. Thus, the contribution of our work is twofold. On the one hand, we present our approach and the experiences we gained during our research. On the other hand, we investigate additional methods that can be used to analyze this multi-source data.

Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications.

The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback.

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios.

Analyzing message streams from social blogging services such as Twitter is a challenging task because of the vast number of documents that are produced daily. At the same time, the availability of geolocated, realtime, and manually created status updates are an invaluable data source for situational awareness scenarios. In this work we present an approach that allows for an interactive analysis of location-based microblog messages in realtime by means of scalable aggregation and geolocated text visualization. For this purpose, we use a novel cluster analysis approach and distinguish between local event reports and global media reaction to detect spatiotemporal anomalies automatically. A workbench allows the scalable visual examination and analysis of messages featuring perspective and semantic layers on a world map representation. Our novel techniques can be used by analysts to classify the presented event candidates and examine them on a global scale.

Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analyst's information need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifier's quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora.

Patents are of growing importance in current economic markets. Analyzing patent information has, therefore, become a common task for many interest groups. As a prerequisite for patent analysis, extensive search for relevant patent information is essential. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. Already the amount of patent data to be analyzed poses challenges with respect to scalability. Further scalability issues arise concerning the diversity of users and the large variety of analysis tasks. With "PatViz", a system for interactive analysis of patent information has been developed addressing scalability at various levels. PatViz provides a visual environment allowing for interactive reintegration of insights into subsequent search iterations, thereby bridging the gap between search and analytic processes. Because of its extensibility, we expect that the approach we have taken can be employed in different problem domains that require high quality of search results regarding their completeness.

Patents are an invaluable source of scientific and technological information. Due to the strongly increasing number of patent applications and the broadening of the objectives of patent analysis, there is a great demand for ubiquitous access to patent information and for flexible visualizations meeting the requirements of different groups of users. In this paper we propose new visualization techniques for patent information and approaches for interactively exploring this information in web based environments. We show how these visualizations can be integrated into existing web portals by using a new paradigm that we call Semantic Lens.