Daniel A. Keim

ColorCAT: Guided Design of Colormaps for Combined Analysis Tasks

Colormap design is challenging because the encoding must match the requirements of data and analysis tasks as well as the perception of the target user. A number of well-known tools exist to support the design of colormaps. ColorBrewer [HB03], for example, is a great resource to select colors for qualitative, sequential, and diverging data. PRAVDAColor [BRT95] and Tominski et al. [TFS08], for example, provide valuable guidelines for single analysis tasks such as localization, identification, and comparison. However, for solving real world problems in most practical applications, single elementary analysis tasks are not sufficient but need to be combined. In this paper, we propose a methodology and tool to design colormaps for combined analysis tasks. We define color mapping requirements and develop a set of design guidelines. The visualization expert is integrated in the design process to incorporate his/her design requirements, which may depend on the application, culture, and aesthetics. Our ColorCAT tool guides novice and expert designers through the creation of colormaps and allows the exploration of the design space of color mapping for combined analysis tasks.

On the Impact of the Medium in the Effectiveness of 3D Software Visualizations

Many visualizations have proven to be effective in supporting various software related tasks. Although multiple media can be used to display a visualization, the standard computer screen is used the most. We hypothesize that the medium has a role in their effectiveness. We investigate our hypotheses by conducting a controlled user experiment. In the experiment we focus on the 3D city visualization technique used for software comprehension tasks. We deploy 3D city visualizations across a standard computer screen (SCS), an immersive 3D environment (I3D), and a physical 3D printed model (P3D). We asked twenty-seven participants (whom we divided in three groups for each medium) to visualize software systems of various sizes, solve a set of uniform comprehension tasks, and complete a questionnaire. We measured the effectiveness of visualizations in terms of performance, recollection, and user experience. We found that even though developers using P3D required the least time to identify outliers, they perceived the least difficulty when visualizing systems based on SCS. Moreover, developers using I3D obtained the highest recollection.

A Visual Analytics Field Experiment to Evaluate Alternative Visualizations for Cyber Security Applications

The analysis and exploration of emerging threats in the Internet is important to better understand the behaviour of attackers and develop new methods to enhance cyber security. Fully automated algorithms alone are often not capable of providing actionable insights about the threat landscape. We therefore combine a multi-criteria clustering algorithm, tailor-made for the identification of such attack campaigns with three interactive visualizations, namely treemap representations, interactive node-link diagrams, and chord diagrams, to allow the analysts to visually explore and make sense of the resulting multi-dimensional clusters. To demonstrate the potential of the system, we share our lessons learned in conducting a field experiment with experts in a security response team and show how it helped them to gain new insights into various threat landscapes.

An Adaptive Image-based Plagiarism Detection Approach

Identifying plagiarized content is a crucial task for educational and research institutions, funding agencies, and academic publishers. Plagiarism detection systems available for productive use reliably identify copied text, or near-copies of text, but often fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. To improve the detection capabilities for disguised forms of academic plagiarism, we analyze the images in academic documents as text-independent features. We propose an adaptive, scalable, and extensible image-based plagiarism detection approach suitable for analyzing a wide range of image similarities that we observed in academic documents. The proposed detection approach integrates established image analysis methods, such as perceptual hashing, with newly developed similarity assessments for images, such as ratio hashing and position-aware OCR text matching. We evaluate our approach using 15 image pairs that are representative of the spectrum of image similarity we observed in alleged and confirmed cases of academic plagiarism. We embed the test cases in a collection of 4,500 related images from academic texts. Our detection approach achieved a recall of 0.73 and a precision of 1. These results indicate that our image-based approach can complement other content-based feature analysis approaches to retrieve potential source documents for suspiciously similar content from large collections. We provide our code as open source to facilitate future research on image-based plagiarism detection.

Feature Alignment for the Analysis of Verbatim Text Transcripts

In the research of deliberative democracy, political scientists are interested in analyzing the communication models of discussions, debates, and mediation processes with the goal of extracting reoccurring discourse patterns from the verbatim transcripts of these conversations. To enhance the time-exhaustive manual analysis of such patterns, we introduce a visual analytics approach that enables the exploration and analysis of repetitive feature patterns over parallel text corpora using feature alignment. Our approach is tailored to the requirements of our domain experts. In this paper, we discuss our visual design and workflow, and we showcase the applicability of our approach using an experimental parallel corpus of political debates.

Provenance-Based Visual Data Exploration with EVLIN

Tools for visual data exploration allow users to visually browse through and analyze datasets to possibly reveal interesting information hidden in the data that users are a priori unaware of. Such tools rely on both query recommendations to select data to be visualized and visualization recommendations for these data to best support users in their visual data exploration process. EVLIN (exploring visually with lineage) is a system that assists users in visually exploring relational data stored in a data warehouse. EVLIN implements novel techniques for recommending both queries and their result visualization in an integrated and interactive way [3]. Recommendations rely on provenance (aka lineage) that describes the production process of displayed data. The demonstration of EVLIN includes an introduction to its features and functionality through sample exploration sessions. Conference attendees will then have the opportunity to gain handson experience of provenance-based visual data exploration by performing their own exploration sessions. These sessions will explore real-world data from several domains. While exploration sessions use a Web-based visual interface, the demonstration also features a researcher console, where attendees may have a look behind the scenes to get a more in-depth understanding of the underlying recommendation algorithms. 1 VISUAL DATA EXPLORATION Data exploration [8] helps users in finding interesting information in data sets when they do not know beforehand what useful information hides in their data. It thus supports humans in understanding and interpreting data in an investigative way. As manual data exploration is tedious, time-consuming, and it is easy to overlook interesting information, there is a need for tools supporting data exploration. These tools typically rely on different kinds of recommendations. Essentially, query recommendation guides users in their investigation of a data set D by suggesting queries as next exploration steps, given an initial query Q . Opposed to that, visualization recommendation commonly determines suited visualizations given a data set as input. State-of-the-art. Most data and visualization recommendation techniques work independently from one another, meaning that the result of query recommendation, i.e., the data set Q ′(D) returned by executing a recommended query Q ′ over D, has no impact on the visualization recommendation process, and vice versa. This becomes apparent in Tab. 1 that summarizes works most closely related to ours. For each approach, it describes (i) the expressiveness of input queries (e.g., select-project-join (SPJ) queries, select-project-aggregate (SPA) queries, or cube queries

Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

A fundamental task in criminal intelligence analysis is to analyze the similarity of crime cases, called comparative case analysis (CCA), to identify common crime patterns and to reason about unsolved crimes. Typically, the data are complex and high dimensional and the use of complex analytical processes would be appropriate. State-of-the-art CCA tools lack flexibility in interactive data exploration and fall short of computational transparency in terms of revealing alternative methods and results. In this paper, we report on the design of the Concept Explorer, a flexible, transparent and interactive CCA system. During this design process, we observed that most criminal analysts are not able to understand the underlying complex technical processes, which decrease the users’ trust in the results and hence a reluctance to use the tool. Our CCA solution implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed visual analytics workflow iteratively supports the interpretation of the results of clustering with the respective feature relations, the development of alternative models, as well as cluster verification. The visualizations offer an understandable and usable way for the analyst to provide feedback to the system and to observe the impact of their interactions. Expert feedback confirmed that our user-centered design decisions made this computational complexity less scary to criminal analysts.

Polarized but illusory beliefs about tap and bottled water: A product- and consumer-oriented survey and blind tasting experiment

BACKGROUNDnDespite the rigorous control of tap water quality, substantial price differences, and environmental concerns, bottled water consumption has increased in recent decades. To facilitate healthy and sustainable consumer choices, a deeper understanding of this water consumption paradox is needed. Therefore, the aim of the two present studies was to examine health-related beliefs and risk perceptions and their accuracy by implementing a combined product- and consumer-oriented approach.nnnMETHODSnAn online survey (Nu202f=u202f578) and a blind taste test (Nu202f=u202f99) assessed perceptions and behaviors for tap and bottled water within primarily tap and bottled water consumers in a fully crossed design. The combined product- and consumer-oriented approach yielded significant consumeru202f×u202fproduct interaction effects.nnnRESULTSnThe two consumer groups showed polarized ratings regarding perceived quality/hygiene, health risks and taste for bottled and tap water, indicating that the two consumer groups substantially diverged in their beliefs. However, in the blind taste test, neither consumer group was able to distinguish tap from bottled water samples (consumer perspective). Moreover, tap or bottled water samples did not systemically vary in their ascribed health-risk or taste characteristics (product perspective).nnnCONCLUSIONSnAlthough the two consumer groups differ greatly in their beliefs, the perceived health risk and taste differences seem to reflect illusionary beliefs rather than actual experiences or product characteristics. Public health campaigns should address these illusions to promote healthy and sustainable consumer choices.

Content-based Analysis and Visualization of Story Complexity

Diagramme spielen auch in der Linguistik eine große Rolle. Ob der Verständlichkeit, mit der Diagramme erstellt und verwendet werden, geht die Reflexion über die diagrammatische Praxis manchmal verloren. Der folgende Beitrag ist ein Plädoyer, diese Praxis aus drei unterschiedlichen Perspektiven zu befragen: Aus diagrammatischer, algorithmischer und wissensgeschichtlicher Perspektive. Dieses Programm einer „Visual Linguistics“ stellt Fragen nach dem Charakter von Diagrammen, dem Status von Diagrammen in Forschungsprozessen und insbesondere dazu, welchen Einfluss Digitalität auf die Visualisierung sprachlicher Phänomene ausübt. Schließlich kann mit Ludwik Fleck die diagrammatische Praxis in Beziehung zu wissenschaftlichen Denkstilen gesetzt werden. Vor dem Hintergrund dieser Überlegungen ergeben sich fünf diagrammatische Grundformen, die bei der Visualisierung von sprachlichen Daten eine wichtige Rolle spielen: Liste, Karte, Partitur, Vektoren, Graph/Netz. Listen und Partituren werden im vorliegenden Beitrag ausführlich diskutiert und es wird gezeigt, welche Rolle sie bei der Gegenstandskonstitution in der Linguistik haben.

VIS4ML : An Ontology for Visual Analytics Assisted Machine Learning

While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely “VA-assisted ML”. The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly.