2023, Jentner, Wolfgang, Lindholz, Giuliana, Schäfer, Hanna, El-Assady, Mennatallah, Ma, Kwan-Liu, Keim, Daniel A.

We present an approach that shows all relevant subspaces of categorical data condensed in a single picture. We model the categorical values of the attributes as co-occurrences with data partitions generated from structured data using pattern mining. We show that these co-occurrences are a-priori allowing us to greatly reduce the search space effectively generating the condensed picture where conventional approaches filter out several subspaces as these are deemed insignificant. The task of identifying interesting subspaces is common but difficult due to exponential search spaces and the curse of dimensionality. One application of such a task might be identifying a cohort of patients defined by attributes such as gender, age, and diabetes type that share a common patient history, which is modeled as event sequences. Filtering the data by these attributes is common but cumbersome and often does not allow a comparison of subspaces. We contribute a powerful multi-dimensional pattern exploration approach (MDPE-approach) agnostic to the structured data type that models multiple attributes and their characteristics as co-occurrences, allowing the user to identify and compare thousands of subspaces of interest in a single picture. In our MDPE-approach, we introduce two methods to dramatically reduce the search space, outputting only the boundaries of the search space in the form of two tables. We implement the MDPE-approach in an interactive visual interface (MDPE-vis) that provides a scalable, pixel-based visualization design allowing the identification, comparison, and sense-making of subspaces in structured data. Our case studies using a gold-standard dataset and external domain experts confirm our approach’s and implementation’s applicability. A third use case sheds light on the scalability of our approach and a user study with 15 participants underlines its usefulness and power.

2018, Jentner, Wolfgang, Sevastjanova, Rita, Stoffel, Florian, Keim, Daniel A., Bernard, Jürgen, El-Assady, Mennatallah

Advanced artificial intelligence models are used to solve complex real-world problems across different domains. While bringing along the expertise for their specific domain problems, users from these various application fields often do not readily understand the underlying artificial intelligence models. The resulting opacity implicates a low level of trust of the domain expert, leading to an ineffective and hesitant usage of the models. We postulate that it is necessary to educate the domain experts to prevent such situations. Therefore, we propose the metaphorical narrative methodology to transitively conflate the mental models of the involved modeling and domain experts. Metaphorical narratives establish an uncontaminated, unambiguous vocabulary that simplifies and abstracts the complex models to explain their main concepts. Elevating the domain experts in their methodological understanding results in trust building and an adequate usage of the models. To foster the methodological understanding, we follow the Visual Analytics paradigm that is known to provide an effective interface for the human and the machine. We ground our proposed methodology on different application fields and theories, detail four successfully applied metaphorical narratives, and discuss important aspects, properties, and pitfalls.

2014, El-Assady, Mennatallah, Jentner, Wolfgang, Stein, Manuel, Fischer, Fabian, Schreck, Tobias, Keim, Daniel A.

We present two original approaches for visual-interactive prediction of user movie ratings and box office gross after the opening weekend, as designed and awarded during VAST Challenge 2013. Our approaches are driven by machine learning models and interactive data exploration, respectively. They consider an array of different training data types, including categorical/discrete data, time series data, and sentiment data from social media. The two approaches are only first steps towards visual-interactive prediction, but have shown to deliver improved prediction results as compared to baseline non-interactive prediction, and may serve as starting points for other predictive applications. Furthermore, an abstract workflow for predictive visual analytics is derived. We also discuss promising challenges for future research in visual-interactive predictive analysis, including design space, evaluation, and model visualization.

2021, Sevastjanova, Rita, Jentner, Wolfgang, Sperrle, Fabian, Kehlbeck, Rebecca, Bernard, Jürgen, El-Assady, Mennatallah

Linguistic insight in the form of high-level relationships and rules in text builds the basis of our understanding of language. However, the data-driven generation of such structures often lacks labeled resources that can be used as training data for supervised machine learning. The creation of such ground-truth data is a time-consuming process that often requires domain expertise to resolve text ambiguities and characterize linguistic phenomena. Furthermore, the creation and refinement of machine learning models is often challenging for linguists as the models are often complex, in-transparent, and difficult to understand. To tackle these challenges, we present a visual analytics technique for interactive data labeling that applies concepts from gamification and explainable Artificial Intelligence (XAI) to support complex classification tasks. The visual-interactive labeling interface promotes the creation of effective training data. Visual explanations of learned rules unveil the decisions of the machine learning model and support iterative and interactive optimization. The gamification-inspired design guides the user through the labeling process and provides feedback on the model performance. As an instance of the proposed technique, we present QuestionComb, a workspace tailored to the task of question classification (i.e., in information-seeking vs. non-information-seeking questions). Our evaluation studies confirm that gamification concepts are beneficial to engage users through continuous feedback, offering an effective visual analytics technique when combined with active learning and XAI.

2017, Jentner, Wolfgang, El-Assady, Mennatallah, Gipp, Bela, Keim, Daniel A.

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.

2013, El-Assady, Mennatallah, Hafner, Daniel, Blumenschein, Michael, Jӓger, Alexander, Jentner, Wolfgang, Rohrdantz, Christian, Fischer, Fabian, Simon, Svenja, Schreck, Tobias, Keim, Daniel A.

This paper describes our solution to the IEEE VAST 2013 Mini Challenge 11. The task of the challenge was to create a visual and interactive tool to predict the popularity of new movies in terms of viewer ratings and ticket sales for the opening weekend in the U.S. The data usage was restricted by the challenge organizers to data from the Internet Movie Database (IMDb)2 and a predefined set of Twitter3 microblog messages. To tackle the challenge we designed a system together with an analysis workflow, combining machine learning and visualization paradigms in order to obtain accurate predictions. In Section 2 we describe the machine learning components used within the analysis workflow. Next, in Section 3, we describe where and how the human analyst is enabled to enhance the prediction with her/his world knowledge. Finally, Section 4 concludes the paper providing an evaluation of the prediction accuracy with and without human intervention.

2019, El-Assady, Mennatallah, Jentner, Wolfgang, Sperrle, Fabian, Sevastjanova, Rita, Hautli-Janisz, Annette, Butt, Miriam, Keim, Daniel A.

We present a modular framework for the rapid-prototyping of linguistic, web-based, visual analytics applications. Our framework gives developers access to a rich set of machine learning and natural language processing steps, through encapsulating them into micro-services and combining them into a computational pipeline. This processing pipeline is auto-configured based on the requirements of the visualization front-end, making the linguistic processing and visualization design, detached independent development tasks. This paper describes the constellation and modality of our framework, which continues to support the efficient development of various human-in-the-loop, linguistic visual analytics research techniques and applications.

2016, El-Assady, Mennatallah, Gold, Valentin, Hautli-Janisz, Annette, Jentner, Wolfgang, Butt, Miriam, Holzinger, Katharina, Keim, Daniel A.

For the last two decades, deliberative democracy has been intensively debated within political science and other related fields. Only recently, deliberation research has experienced a computational turn. In this paper, we present a linguistic and visual framework for the study of deliberative communication. The framework includes a range of visual analytics approaches to support research into deliberation. In particular, we propose a range of visualizations for highlighting deliberative patterns over time, speakers, and debates.