Automated visualization generation tools make visualization authoring more accessible to non-programmers and accelerate expert visualization designers in their work. Yet, combining these functions in one system remain...
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ISBN:
(数字)9798350380163
ISBN:
(纸本)9798350380170
Automated visualization generation tools make visualization authoring more accessible to non-programmers and accelerate expert visualization designers in their work. Yet, combining these functions in one system remains a challenge for the research community because it has to maintain a high level of expressiveness and facilitate several tasks for diverse background users while remaining simple and intuitive. Providing a system that handles multiple tasks for heterogeneous user groups could be achieved, on the one hand, through unrestricted user input in the form of natural language. On the other hand, introducing a certain level of abstraction on multiple levels of the system can help integrate more tasks than implementing them separately. In this work, we present the concept of such a system with an LLM-based user query handling and mapping of the extracted components of the user task onto visual design choices with the help of deep learning. Our trained neural network shows promising results, suggesting the generalizability of the proposed approach.
visualanalytics combines human cognitive processes and abilities with computational models to solve complex problems by using the strengths of computers and humans. Decision-making processes are commonly very complex...
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ISBN:
(数字)9798350380163
ISBN:
(纸本)9798350380170
visualanalytics combines human cognitive processes and abilities with computational models to solve complex problems by using the strengths of computers and humans. Decision-making processes are commonly very complex and involve investigating various factors and indicators. By integrating visualanalytics into the decision-making process, the human cognitive load can be reduced, and the accuracy of decisions could be higher. This might be why many visual decision support systems exist, and visualanalytics approaches provide decision support. However, the literature review reveals that there are either structured decision support methods integrating statistical approaches or visualanalytics methods that are commonly designed for exploration, comparison, and analysis to support decision-making. By investigating the core ideas related to human limitations and programmed decision-making, we propose in this paper a novel visualanalytics approach to support structured and exploratory approaches by integrating Multi-Criteria Decision Making (MCDM) methods in visualanalytics. To reduce the common expert judgment in such methods, our approach trains a neural network through the interaction with the system. Our contributions are threefold: providing a systematic view of decision-making processes, discussing the dual paradigms of visual decision support systems, and proposing a visualanalytics approach that leverages MCDM and neural networks for improved decision support.
The evolving digitization of Germany's medical care provides new opportunities for computer-supported patient treatment. Particularly, resident medical doctors can be empowered to gather comprehensive information ...
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ISBN:
(数字)9798350380163
ISBN:
(纸本)9798350380170
The evolving digitization of Germany's medical care provides new opportunities for computer-supported patient treatment. Particularly, resident medical doctors can be empowered to gather comprehensive information about the disease history, medications, and other important indicators quickly and increase the quality of treatment while maintaining the time for treatment. The central element that enables such computer-assisted support in the treatment is electronic health records (EHRs). EHRs facilitate digital access to vast medical data repositories in digital format that were previously confined to analog forms in medical facilities. However, using EHRs in daily medical treatment is time-consuming due to their unstructured textual format. There is a pressing need for analytical tools that extract the most important information from EHRs, provide that information in a quickly comprehensible way, and synthesize the entire patient history for a reliable medical treatment. In this work, we propose an innovative visualanalytics approach and system specifically designed for medical care. Our approach and the implemented system seamlessly integrate interactive visualizations with fitting pre-trained transformer models to assist medical professionals in consolidating and presenting intricate patient data through comprehensible interactive visual interfaces. Utilizing transformer-based information extraction, it carefully manages the shift from digitized medical documents to quickly accessible patient information in a dynamic and interactive visual interface.
Climate change is one of the greatest challenges of our time and affects all areas of our society. Climate science is a complex field and the majority of the public does not experience the effects of climate change di...
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ISBN:
(数字)9798350380163
ISBN:
(纸本)9798350380170
Climate change is one of the greatest challenges of our time and affects all areas of our society. Climate science is a complex field and the majority of the public does not experience the effects of climate change directly but through media. Social media platforms are among the most important communication channels: millions of people exchange information and interact with content on these platforms every day. For this reason, knowledge about the development of climate communication is relevant for various disciplines, but especially for communication sciences. How social media users interact with content on climate change and what content they consume is important for adapting communication strategies. This enables communicators such as journalists to respond appropriately to user interests and align their content. We contribute with a novel approach to portray climate change behavior of social media, transformer-based speech extraction from web video, transformer-based information extraction and trend analysis, and an interactive visual interface showing topic-specific data and trends.
Interactive machine learning enhances data pre-processing, feature engineering, and modeling through humaninteractions. As interactive machine learning becomes more in-corporated into visualanalytics, new pipelines,...
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ISBN:
(数字)9798350380163
ISBN:
(纸本)9798350380170
Interactive machine learning enhances data pre-processing, feature engineering, and modeling through humaninteractions. As interactive machine learning becomes more in-corporated into visualanalytics, new pipelines, concepts, and task taxonomies rise. A common technique for managing complexity in visual analytic systems is multiple-linked views. By now no comprehensive concept exists for multiple-linked views across all stages of the visual analytic pipeline. Therefore, we introduce a conceptual model that shows the possible interactions between humans and computers at each step of the VA pipeline. Our main contribution is a coordination model for linking multiple views across each layer of the VA pipeline.
Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows react...
Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel visualanalytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on macro level, micro level, and monitoring topics of interest.
Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potentia...
Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. visualanalytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing visualanalytics with Corporate Foresight does not exist yet. We propose in this work a holistic visualanalytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised visualanalytics model and its proof of concept through implementation as a web-based system with real data.
visualanalytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into visualanalytics h...
visualanalytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into visualanalytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. visualanalytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, visualanalytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in visualanalytics, focusing on both explainable artificial intelligence in visualanalytics and the human knowledge generation process through visualanalytics. We review explainable artificial intelligence approaches in visualanalytics and propose a revised visualanalytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in visualanalytics from two time periods to highlight recently used artificial intelligence approaches in visualanalytics. Based on this review, we propose a revised task model for tasks in visualanalytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in visualanalytics, a revised model for creating explainable artificial intelligence through visualanalytics, a screening review of recent artificial intelligence methods in visualanalytics, and a revised task model for generic tasks in visualanalytics.
The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using re...
The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel visualanalytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a visualanalytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.
Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although...
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ISBN:
(纸本)9781665490085
Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a visualanalytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel visualanalytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.
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