2025 |
| 162. | Rita Francese; Kawa Nazemi; Laura Desantis; Mariacarla Staffa; Felice Iasevoli EMpowering PAtients THrough AI, Multimedia, and Explainable HCI: Innovations in Personalized Healthcare (EMPATH-IA) Konferenzbeitrag In: Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter, S. 1–3, ACM, 2025. @inproceedings{Francese2025,
title = {EMpowering PAtients THrough AI, Multimedia, and Explainable HCI: Innovations in Personalized Healthcare (EMPATH-IA)},
author = {Rita Francese and Kawa Nazemi and Laura Desantis and Mariacarla Staffa and Felice Iasevoli},
doi = {10.1145/3750069.3757876},
year = {2025},
date = {2025-10-01},
booktitle = {Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter},
pages = {1--3},
publisher = {ACM},
series = {CHItaly 2025},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 161. | Adrian Rusu; Nuno Datia; Kawa Nazemi; Quang Vinh Nguyen Visualization, Visual Analytics, and Explainability Artikel In: Information Visualization, 2025, ISSN: 1473-8724. @article{Rusu2025,
title = {Visualization, Visual Analytics, and Explainability},
author = {Adrian Rusu and Nuno Datia and Kawa Nazemi and Quang Vinh Nguyen},
url = {https://journals.sagepub.com/doi/10.1177/14738716251367674, Full text},
doi = {10.1177/14738716251367674},
issn = {1473-8724},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-01},
journal = {Information Visualization},
publisher = {SAGE Publications},
abstract = {Visualization, visual analytics, and explainability are interconnected concepts that enhance understanding and decision-making through data. Visualization involves the graphical representation of data to reveal patterns, trends, and insights that might be difficult to detect in raw numbers. Visual analytics combines automated analysis techniques with interactive visualizations, enabling users to explore complex datasets, generate hypotheses, and draw conclusions more effectively. Explainability, in the context of machine learning and AI, refers to the ability to interpret and understand the decisions made by algorithms. Together, these topics help create tools to support better insights, foster trust, and enhance human-machine collaboration.},
keywords = {},
pubstate = {published},
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Visualization, visual analytics, and explainability are interconnected concepts that enhance understanding and decision-making through data. Visualization involves the graphical representation of data to reveal patterns, trends, and insights that might be difficult to detect in raw numbers. Visual analytics combines automated analysis techniques with interactive visualizations, enabling users to explore complex datasets, generate hypotheses, and draw conclusions more effectively. Explainability, in the context of machine learning and AI, refers to the ability to interpret and understand the decisions made by algorithms. Together, these topics help create tools to support better insights, foster trust, and enhance human-machine collaboration. |
| 160. | Cristian A Secco; Kawa Nazemi A modular visual analytics dashboard for patient health data Artikel In: Information Visualization, 2025, ISSN: 1473-8724. @article{Secco2025,
title = {A modular visual analytics dashboard for patient health data},
author = {Cristian A Secco and Kawa Nazemi},
url = {https://journals.sagepub.com/doi/10.1177/14738716251360341},
doi = {10.1177/14738716251360341},
issn = {1473-8724},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-01},
journal = {Information Visualization},
publisher = {SAGE Publications},
abstract = {In everyday outpatient care, office-based medical professionals often face a growing volume of patient records, spanning years or even decades, while consultation times remain critically short. Much of this information is embedded in unstructured documents such as physician letters, lab reports, and diagnostic notes. The lack of time makes it difficult for physicians to comprehend the patients’ medical history and to base decisions on the full health and disease history. This paper presents a modular Visual Analytics approach particularly designed to support general practitioners in navigating long-term patient histories. Using transformer-based large language models, the system automatically extracts and classifies relevant clinical entities from unstructured documents. These extracted elements are mapped to a set of predefined data categories and visualized through tailored, interactive components, such as medication intake timelines, blood count diagrams, and named-entity highlighted texts. Visual components are selected dynamically based on content type and clinical relevance, allowing medical professionals to explore patient histories efficiently and to identify critical information at a glance. The modular architecture facilitates integrating new document types and visual modules, ensuring flexibility and scalability. Designed for real-world applicability, the system aims to reduce cognitive load, improve information accessibility, and support time-sensitive decision-making in primary care.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In everyday outpatient care, office-based medical professionals often face a growing volume of patient records, spanning years or even decades, while consultation times remain critically short. Much of this information is embedded in unstructured documents such as physician letters, lab reports, and diagnostic notes. The lack of time makes it difficult for physicians to comprehend the patients’ medical history and to base decisions on the full health and disease history. This paper presents a modular Visual Analytics approach particularly designed to support general practitioners in navigating long-term patient histories. Using transformer-based large language models, the system automatically extracts and classifies relevant clinical entities from unstructured documents. These extracted elements are mapped to a set of predefined data categories and visualized through tailored, interactive components, such as medication intake timelines, blood count diagrams, and named-entity highlighted texts. Visual components are selected dynamically based on content type and clinical relevance, allowing medical professionals to explore patient histories efficiently and to identify critical information at a glance. The modular architecture facilitates integrating new document types and visual modules, ensuring flexibility and scalability. Designed for real-world applicability, the system aims to reduce cognitive load, improve information accessibility, and support time-sensitive decision-making in primary care. |
| 159. | Egils Ginters; Uga Dumpis; Laura Calvet Liñán; Miquel Angel Piera Eroles; Kawa Nazemi; Andrejs Matvejevs; Mario Arturo Ruiz Estrada A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks Artikel In: Mathematics, Bd. 13, Nr. 1, 2025, ISSN: 2227-7390. @article{math13010091,
title = {A Paradigm for Modeling Infectious Diseases: Assessing Malware Spread in Early-Stage Outbreaks},
author = {Egils Ginters and Uga Dumpis and Laura Calvet Liñán and Miquel Angel Piera Eroles and Kawa Nazemi and Andrejs Matvejevs and Mario Arturo Ruiz Estrada},
url = {https://www.mdpi.com/2227-7390/13/1/91},
doi = {10.3390/math13010091},
issn = {2227-7390},
year = {2025},
date = {2025-01-01},
journal = {Mathematics},
volume = {13},
number = {1},
abstract = {As digitalization and artificial intelligence advance, cybersecurity threats intensify, making malware—a type of software installed without authorization to harm users—an increasingly urgent concern. Due to malware’s social and economic impacts, accurately modeling its spread has become essential. While diverse models exist for malware propagation, their selection tends to be intuitive, often overlooking the unique aspects of digital environments. Key model choices include deterministic vs. stochastic, planar vs. spatial, analytical vs. simulation-based, and compartment-based vs. individual state-tracking models. In this context, our study assesses fundamental infection spread models to determine those most applicable to malware propagation. It is organized in two parts: the first examines principles of deterministic and stochastic infection models, and the second provides a comparative analysis to evaluate model suitability. Key criteria include scalability, robustness, complexity, workload, transparency, and manageability. Using consistent initial conditions, control examples are analyzed through Python-based numerical methods and agent-based simulations in NetLogo. The findings yield practical insights and recommendations, offering valuable guidance for researchers and cybersecurity professionals in applying epidemiological models to malware spread.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As digitalization and artificial intelligence advance, cybersecurity threats intensify, making malware—a type of software installed without authorization to harm users—an increasingly urgent concern. Due to malware’s social and economic impacts, accurately modeling its spread has become essential. While diverse models exist for malware propagation, their selection tends to be intuitive, often overlooking the unique aspects of digital environments. Key model choices include deterministic vs. stochastic, planar vs. spatial, analytical vs. simulation-based, and compartment-based vs. individual state-tracking models. In this context, our study assesses fundamental infection spread models to determine those most applicable to malware propagation. It is organized in two parts: the first examines principles of deterministic and stochastic infection models, and the second provides a comparative analysis to evaluate model suitability. Key criteria include scalability, robustness, complexity, workload, transparency, and manageability. Using consistent initial conditions, control examples are analyzed through Python-based numerical methods and agent-based simulations in NetLogo. The findings yield practical insights and recommendations, offering valuable guidance for researchers and cybersecurity professionals in applying epidemiological models to malware spread. |
2024 |
| 158. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Melanie Siegel; Kawa Nazemi Real-Time Ideation Analyzer and Information Recommender Artikel In: Electronics, Bd. 13, Nr. 9, 2024, ISSN: 2079-9292. @article{electronics13091761,
title = {Real-Time Ideation Analyzer and Information Recommender},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Melanie Siegel and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/13/9/1761},
doi = {10.3390/electronics13091761},
issn = {2079-9292},
year = {2024},
date = {2024-05-15},
urldate = {2024-05-15},
journal = {Electronics},
volume = {13},
number = {9},
abstract = {The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas. |
| 157. | Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Guided Visual Analytics-A Visual Analytics Guidance Approach for Systematic Reviews in Research Buchkapitel In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Hrsg.): Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery, S. 319–343, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-46549-9. @inbook{SSM*24,
title = {Guided Visual Analytics-A Visual Analytics Guidance Approach for Systematic Reviews in Research},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-031-46549-9_11},
doi = {10.1007/978-3-031-46549-9_11},
isbn = {978-3-031-46549-9},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery},
pages = {319--343},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Visual Analytics systems are often complex expert systems that require high expertise. The simplification of the interaction with such systems in order to make them usable for novices is one subject of current research works. One way to ease the user's interaction with the systems is through guidance approaches. Guidance approaches aim to support the user while working with the system by providing targeted assistance. We present in this work a stepwise guidance approach for Visual Analytics. For that, we use the domain of literature search and exploration exemplary. The underlying system allows researchers to visually search and explore scientific publications and automatically generate systematic review protocols. To accomplish this, we present a stepwise visual guidance system approach that combines automatic steps and manual user validation to unify the systematic literature review (SLR) creation process. Based on a design study we conducted, we present our proposed AI-based assistant (MAIA) that assists users in the various steps required to create systematic literature reviews. According to the PRISMA statement, we describe the process of SLR creation exemplary and present the different screens that guide the user through SLR creation.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Visual Analytics systems are often complex expert systems that require high expertise. The simplification of the interaction with such systems in order to make them usable for novices is one subject of current research works. One way to ease the user's interaction with the systems is through guidance approaches. Guidance approaches aim to support the user while working with the system by providing targeted assistance. We present in this work a stepwise guidance approach for Visual Analytics. For that, we use the domain of literature search and exploration exemplary. The underlying system allows researchers to visually search and explore scientific publications and automatically generate systematic review protocols. To accomplish this, we present a stepwise visual guidance system approach that combines automatic steps and manual user validation to unify the systematic literature review (SLR) creation process. Based on a design study we conducted, we present our proposed AI-based assistant (MAIA) that assists users in the various steps required to create systematic literature reviews. According to the PRISMA statement, we describe the process of SLR creation exemplary and present the different screens that guide the user through SLR creation. |
| 156. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Similarity in Visual Analytics-A Visual Analytics Approach for Finding Similar Publications Buchkapitel In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Hrsg.): Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery, S. 443–468, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-46549-9. @inbook{Blazevic2024,
title = {Similarity in Visual Analytics-A Visual Analytics Approach for Finding Similar Publications},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-031-46549-9_16},
doi = {10.1007/978-3-031-46549-9_16},
isbn = {978-3-031-46549-9},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery},
pages = {443--468},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Recent studies show that the search for relevant publications requires researchers to invest a significant amount of time and work; some studies even underline that this task requires the most time and work out of all the tasks in the entire research process. To reduce the search time in the research process, we propose a visual analytics approach that combines models and methods of natural language process, machine learning, similarity measures, and interactive visual representations. The proposed approach is based on our previous works and enhances those with additional automatic assistance during the entire search process. Our visual analytics approach facilitates the presentation of large amounts of relevant results similar to the identified topics of interest and tailored to the needs of researchers during their research process. The proposed method enables annotation in the context of exploration, allowing researchers to quickly find and bookmark relevant publications during exploration and, by doing so, improve the publication recommendations. We analyze the annotations researchers make throughout their research journey to identify the topics of interest and use them as input for our learning and measurement methods. By utilizing researchers' commonly observed annotation and exploration behavior, our approach counters information overload by generating labeled vectors of interest and providing similar publications.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Recent studies show that the search for relevant publications requires researchers to invest a significant amount of time and work; some studies even underline that this task requires the most time and work out of all the tasks in the entire research process. To reduce the search time in the research process, we propose a visual analytics approach that combines models and methods of natural language process, machine learning, similarity measures, and interactive visual representations. The proposed approach is based on our previous works and enhances those with additional automatic assistance during the entire search process. Our visual analytics approach facilitates the presentation of large amounts of relevant results similar to the identified topics of interest and tailored to the needs of researchers during their research process. The proposed method enables annotation in the context of exploration, allowing researchers to quickly find and bookmark relevant publications during exploration and, by doing so, improve the publication recommendations. We analyze the annotations researchers make throughout their research journey to identify the topics of interest and use them as input for our learning and measurement methods. By utilizing researchers' commonly observed annotation and exploration behavior, our approach counters information overload by generating labeled vectors of interest and providing similar publications. |
| 155. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science Buchkapitel In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Hrsg.): Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery, S. 345–373, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-46549-9. @inbook{Blazevic2024b,
title = {Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-031-46549-9_12},
doi = {10.1007/978-3-031-46549-9_12},
isbn = {978-3-031-46549-9},
year = {2024},
date = {2024-01-01},
booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery},
pages = {345--373},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Studies have shown rising interest in scientific collaborations throughout the past decades. The challenges throughout various studies show an emerging need for research and development in methods and systems that utilize artificial intelligence to provide research communities with adequate tools that facilitate and encourage collaborative research. Many platforms focus on listing authors' publications and showcasing them with citation scores. They neglect the possibility of creating a holistic assistance and collaborative approach that covers the entire scientific research process using adequate intelligence methods. We introduce in this chapter a novel approach to visual collaboration. Our approach covers the entire process of scientific paper writing through real-time visual recommendations. It combines on-the-fly similarity measurements, ideation assistance based on group constellations, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the integration of visual analytics that combines artificial intelligence with interactive visualizations. Thereby the interaction design and the ease of use are essential. Our approach presents a single-source AI-driven visual collaborative research platform for the entire research community.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Studies have shown rising interest in scientific collaborations throughout the past decades. The challenges throughout various studies show an emerging need for research and development in methods and systems that utilize artificial intelligence to provide research communities with adequate tools that facilitate and encourage collaborative research. Many platforms focus on listing authors' publications and showcasing them with citation scores. They neglect the possibility of creating a holistic assistance and collaborative approach that covers the entire scientific research process using adequate intelligence methods. We introduce in this chapter a novel approach to visual collaboration. Our approach covers the entire process of scientific paper writing through real-time visual recommendations. It combines on-the-fly similarity measurements, ideation assistance based on group constellations, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the integration of visual analytics that combines artificial intelligence with interactive visualizations. Thereby the interaction design and the ease of use are essential. Our approach presents a single-source AI-driven visual collaborative research platform for the entire research community. |
| 154. | Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Banissi (Hrsg.) Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery Buch Springer Nature Switzerland, 2024, ISSN: 1860-9503. @book{2024,
title = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery},
editor = {Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Banissi},
url = {http://dx.doi.org/10.1007/978-3-031-46549-9},
doi = {10.1007/978-3-031-46549-9},
issn = {1860-9503},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Studies in Computational Intelligence},
publisher = {Springer Nature Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
|
| 153. | Cristian A. Secco; Lennart B. Sina; Kawa Nazemi Medical Visual Analytics - An Interactive Approach for Analyzing Electronic Health Records Konferenzbeitrag In: 2024 28th International Conference Information Visualisation (IV), S. 143-149, 2024. @inproceedings{10714032,
title = {Medical Visual Analytics - An Interactive Approach for Analyzing Electronic Health Records},
author = {Cristian A. Secco and Lennart B. Sina and Kawa Nazemi},
doi = {10.1109/IV64223.2024.00034},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 28th International Conference Information Visualisation (IV)},
pages = {143-149},
abstract = {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 Visual Analytics 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.},
keywords = {Visual analytics;Medical treatment;Data visualization;Pressing;Transformers;Information retrieval;History;Data mining;Reliability;Electronic medical records;Visual Analytics;Information Extraction;Transformer;Electronic Health Records;Decision-Support;Patient Care},
pubstate = {published},
tppubtype = {inproceedings}
}
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 Visual Analytics 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. |
| 152. | Kawa Nazemi; Cristian A. Secco; Lennart B. Sina; Uliana Eliseeva; Elena Correll; Midhad Blazevic Visual Analytics for Decision-Making Konferenzbeitrag In: 2024 28th International Conference Information Visualisation (IV), S. 150-159, 2024. @inproceedings{10714256,
title = {Visual Analytics for Decision-Making},
author = {Kawa Nazemi and Cristian A. Secco and Lennart B. Sina and Uliana Eliseeva and Elena Correll and Midhad Blazevic},
doi = {10.1109/IV64223.2024.00035},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 28th International Conference Information Visualisation (IV)},
pages = {150-159},
abstract = {Visual Analytics 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 Visual Analytics 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 Visual Analytics approaches provide decision support. However, the literature review reveals that there are either structured decision support methods integrating statistical approaches or Visual Analytics 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 Visual Analytics approach to support structured and exploratory approaches by integrating Multi-Criteria Decision Making (MCDM) methods in Visual Analytics. 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 Visual Analytics approach that leverages MCDM and neural networks for improved decision support.},
keywords = {Decision support systems;Training;Accuracy;Cognitive processes;Visual analytics;Refining;Neural networks;Computer architecture;MCDM;Testing;Visual Analytics;Decision-Making;Decision Support Systems;DSS;Multi-Criteria Decision Making;MCDM},
pubstate = {published},
tppubtype = {inproceedings}
}
Visual Analytics 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 Visual Analytics 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 Visual Analytics approaches provide decision support. However, the literature review reveals that there are either structured decision support methods integrating statistical approaches or Visual Analytics 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 Visual Analytics approach to support structured and exploratory approaches by integrating Multi-Criteria Decision Making (MCDM) methods in Visual Analytics. 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 Visual Analytics approach that leverages MCDM and neural networks for improved decision support. |
| 151. | Elena Correll; Uliana Eliseeva; Kawa Nazemi Visual Analytics for Interactive Machine Learning - A Modular Multi-View Approach Konferenzbeitrag In: 2024 28th International Conference Information Visualisation (IV), S. 1-7, 2024. @inproceedings{10714294,
title = {Visual Analytics for Interactive Machine Learning - A Modular Multi-View Approach},
author = {Elena Correll and Uliana Eliseeva and Kawa Nazemi},
doi = {10.1109/IV64223.2024.00036},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 28th International Conference Information Visualisation (IV)},
pages = {1-7},
abstract = {Interactive machine learning enhances data pre-processing, feature engineering, and modeling through human interactions. As interactive machine learning becomes more in-corporated into visual analytics, 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.},
keywords = {Computers;Analytical models;Visual analytics;Computational modeling;Pipelines;Taxonomy;Refining;Decision making;Machine learning;Data models;Visual Analytics;Interactive Machine Learning;Multiple-Linked Views},
pubstate = {published},
tppubtype = {inproceedings}
}
Interactive machine learning enhances data pre-processing, feature engineering, and modeling through human interactions. As interactive machine learning becomes more in-corporated into visual analytics, 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. |
| 150. | Uliana Eliseeva; Simon Heiß; Kawa Nazemi Query-to- Vis: Conceptualization of a Broad-coverage Automated Visualization Pipeline Konferenzbeitrag In: 2024 28th International Conference Information Visualisation (IV), S. 174-179, 2024. @inproceedings{10714266,
title = {Query-to- Vis: Conceptualization of a Broad-coverage Automated Visualization Pipeline},
author = {Uliana Eliseeva and Simon Heiß and Kawa Nazemi},
doi = {10.1109/IV64223.2024.00038},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 28th International Conference Information Visualisation (IV)},
pages = {174-179},
abstract = {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.},
keywords = {Deep learning;Visualization;Pipelines;Neural networks;Natural languages;Diversity reception;automated visualization;neural networks;user query;LLM},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 149. | Vanessa Kokoschka; Cristian A. Secco; Kawa Nazemi Visual Analytics - Climate Change in Social Media Konferenzbeitrag In: 2024 28th International Conference Information Visualisation (IV), S. 167-173, 2024. @inproceedings{10714267,
title = {Visual Analytics - Climate Change in Social Media},
author = {Vanessa Kokoschka and Cristian A. Secco and Kawa Nazemi},
doi = {10.1109/IV64223.2024.00037},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 28th International Conference Information Visualisation (IV)},
pages = {167-173},
abstract = {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.},
keywords = {Climate change;Visual analytics;Social networking (online);Video on demand;Market research;Data visualization;Metadata;Visual analytics;Meteorology;Visual Analytics;Trend Detection;Transformer;Speech to Text;Climate Change;Social Media},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
2023 |
| 148. | Cristian A. Secco; Lennart B. Sina; Midhad Blazevic; Kawa Nazemi Visual Analytics for Forecasting Technological Trends from Text Konferenzbeitrag In: Proceedings of the 27th International Conference Information Visualisation (IV2023), S. 251-258, IEEE CPS, 2023. @inproceedings{Secco2023,
title = {Visual Analytics for Forecasting Technological Trends from Text},
author = {Cristian A. Secco and Lennart B. Sina and Midhad Blazevic and Kawa Nazemi },
doi = {10.1109/IV60283.2023.00051},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},
pages = {251-258},
publisher = {IEEE CPS},
abstract = {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 Visual Analytics 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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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 Visual Analytics 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. |
| 147. | Kawa Nazemi Artificial Intelligence in Visual Analytics Konferenzbeitrag In: Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award, S. 230 - 237, IEEE CPS, 2023. @inproceedings{Nazemi2023,
title = {Artificial Intelligence in Visual Analytics},
author = {Kawa Nazemi},
doi = {10.1109/IV60283.2023.00048},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award},
journal = {Proceedings of the 27th International Conference Information Visualisation (IV2023) - Best Paper Award-},
pages = {230 - 237},
publisher = {IEEE CPS},
abstract = {Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics 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 Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.},
keywords = {Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual Tasks},
pubstate = {published},
tppubtype = {inproceedings}
}
Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics 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 Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics. |
| 146. | Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Visual Analytics for Corporate Foresight - A Conceptual Approach Konferenzbeitrag In: Proceedings of the 27th International Conference Information Visualisation (IV2023), S. 244-250, IEEE CPS, 2023. @inproceedings{SinaIV2023,
title = {Visual Analytics for Corporate Foresight - A Conceptual Approach},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00050},
year = {2023},
date = {2023-11-29},
urldate = {2023-11-29},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},
pages = {244-250},
publisher = {IEEE CPS},
abstract = {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. Visual Analytics 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 Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics 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 Visual Analytics model and its proof of concept through implementation as a web-based system with real data.},
keywords = {Artificial Intelligence, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. Visual Analytics 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 Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics 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 Visual Analytics model and its proof of concept through implementation as a web-based system with real data. |
| 145. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science Konferenzbeitrag In: Proceedings of the 27th International Conference Information Visualisation (IV 2023), S. 259- 267, IEEE CPS, 2023. @inproceedings{blaz2023,
title = {Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00052},
year = {2023},
date = {2023-11-24},
urldate = {2023-11-24},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV 2023)},
pages = {259- 267},
publisher = {IEEE CPS},
abstract = {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 Visual Analytics 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 Visual Analytics 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.},
keywords = {Artifical Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
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 Visual Analytics 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 Visual Analytics 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. |
| 144. | Ebad Banissi; Harri Siirtola; Anna Ursyn; João Moura Pires; Nuno Datia; Kawa Nazemi; Boris Kovalerchuk; Razvan Andonie; Minoru Nakayama; Marco Temperini; Filippo Sciarrone; Quang Vinh Nguyen; Mabule Samuel Mabakane; Adrian Rusu; Urska Cvek; Marjan Trutschl; Heimo Mueller; Rita Francese; Fatma Boua-li; Gilles Venturini (Hrsg.) Proceedings of 2023 27th International Conference Information Visualisation Konferenzbericht 2023, ISBN: 979-8-3503-4161-4. @proceedings{Banissi2023,
title = {Proceedings of 2023 27th International Conference Information Visualisation},
editor = {Ebad Banissi and Harri Siirtola and Anna Ursyn and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and Razvan Andonie and Minoru Nakayama and Marco Temperini and Filippo Sciarrone and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Urska Cvek and Marjan Trutschl and Heimo Mueller and Rita Francese and Fatma Boua-li and Gilles Venturini },
doi = {10.1109/IV60283.2023.00001},
isbn = {979-8-3503-4161-4},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
issue = {IV2023},
abstract = {Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt.},
keywords = {Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {proceedings}
}
Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt. |
| 143. | Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Hybrid Forecasting Methods - A Systematic Review Artikel In: Electronics, Bd. 12, Nr. 7, 2023. @article{SSB*23,
title = {Hybrid Forecasting Methods - A Systematic Review},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/12/9/2019},
doi = {10.3390/electronics12092019},
year = {2023},
date = {2023-04-27},
urldate = {2023-04-27},
journal = {Electronics},
volume = {12},
number = {7},
abstract = {Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.},
keywords = {hybrid forecsting, PRISMA study},
pubstate = {published},
tppubtype = {article}
}
Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization. |
| 142. | Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System Artikel In: Electronics, Bd. 12, Nr. 7, 2023, ISSN: 2079-9292. @article{electronics12071699,
title = {Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System},
author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},
url = {https://www.mdpi.com/2079-9292/12/7/1699},
doi = {10.3390/electronics12071699},
issn = {2079-9292},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Electronics},
volume = {12},
number = {7},
abstract = {Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.},
keywords = {latex editor, publication recommendations, recommendation systems, similarity algorithms, topics modeling},
pubstate = {published},
tppubtype = {article}
}
Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities. |
| 141. | Mina Schütz Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations Konferenzbeitrag In: Jaap Kamps; Lorraine Goeuriot; Fabio Crestani; Maria Maistro; Hideo Joho; Brian Davis; Cathal Gurrin; Udo Kruschwitz; Annalina Caputo (Hrsg.): Advances in Information Retrieval, S. 468–475, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-28241-6. @inproceedings{Sc23,
title = {Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations},
author = {Mina Schütz},
editor = {Jaap Kamps and Lorraine Goeuriot and Fabio Crestani and Maria Maistro and Hideo Joho and Brian Davis and Cathal Gurrin and Udo Kruschwitz and Annalina Caputo},
doi = {10.1007/978-3-031-28241-6_54},
isbn = {978-3-031-28241-6},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Advances in Information Retrieval},
pages = {468--475},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The automatic detection of disinformation has gained an increased focus by the research community during the last years. The spread of false information can be an issue for political processes, opinion mining and journalism in general. In this dissertation, I propose a novel approach to gain new insights on the automatic detection of disinformation in textual content. Additionally, I will combine multiple research domains, such as fake news, hate speech, propaganda, and extremism. For this purpose, I will create two novel and annotated datasets in German - a large multi-label dataset for disinformation detection in news articles and a second dataset for hate speech detection in social media posts, which both can be used for training the models in the listed domains via transfer learning. With the usage of transfer learning, an extensive data analysis and classification of the presented domains will be conducted. The classification models will be enhanced during and after training using a knowledge graph, containing additional information (i.e. named entities, relationships, topics), to find explicit insights about the common traits or lines of disinformative arguments in an article. Lastly, methods of explainable artificial intelligence will be combined with visualization techniques to understand the models predictions and present the results in a user-friendly and interactive way.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The automatic detection of disinformation has gained an increased focus by the research community during the last years. The spread of false information can be an issue for political processes, opinion mining and journalism in general. In this dissertation, I propose a novel approach to gain new insights on the automatic detection of disinformation in textual content. Additionally, I will combine multiple research domains, such as fake news, hate speech, propaganda, and extremism. For this purpose, I will create two novel and annotated datasets in German - a large multi-label dataset for disinformation detection in news articles and a second dataset for hate speech detection in social media posts, which both can be used for training the models in the listed domains via transfer learning. With the usage of transfer learning, an extensive data analysis and classification of the presented domains will be conducted. The classification models will be enhanced during and after training using a knowledge graph, containing additional information (i.e. named entities, relationships, topics), to find explicit insights about the common traits or lines of disinformative arguments in an article. Lastly, methods of explainable artificial intelligence will be combined with visualization techniques to understand the models predictions and present the results in a user-friendly and interactive way. |
| 140. | Christoph Demus; Mina Schütz; Nadine Probol; Jonas Pitz; Melanie Siegel; Dirk Labudde Hass im Netz -- Aggressivität und Toxizität von Hasskommentaren und Postings, Detektion und Analyse Buchkapitel In: Thomas-Gabriel Rüdiger; P. Saskia Bayerl (Hrsg.): Handbuch Cyberkriminologie, S. 1–32, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-35450-3. @inbook{DSPP23,
title = {Hass im Netz -- Aggressivität und Toxizität von Hasskommentaren und Postings, Detektion und Analyse},
author = {Christoph Demus and Mina Schütz and Nadine Probol and Jonas Pitz and Melanie Siegel and Dirk Labudde},
editor = {Thomas-Gabriel Rüdiger and P. Saskia Bayerl},
url = {https://doi.org/10.1007/978-3-658-35450-3_13-1},
doi = {10.1007/978-3-658-35450-3_13-1},
isbn = {978-3-658-35450-3},
year = {2023},
date = {2023-01-01},
booktitle = {Handbuch Cyberkriminologie},
pages = {1--32},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
abstract = {Hass und aggressives Verhalten im Netz werden immer größere Probleme. Der bisher etablierte Versuch zur Lösung des Problems ist das Löschen von Kommentaren, doch um dem grundlegenden Problem entgegenzuwirken, müssen Ursachen für die Entstehung von Hass im Netz bekämpft werden. In diesem Kapitel wird daher neben Grundlagen der Hatespeechanalyse insbesondere auf Gruppierungen, Informationsfluss und die Ausbreitung von Hass in sozialen Netzwerken eingegangen. Daraus werden dann Maßnahmen zur Bekämpfung der Ursachen von Hass abgeleitet und diskutiert.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Hass und aggressives Verhalten im Netz werden immer größere Probleme. Der bisher etablierte Versuch zur Lösung des Problems ist das Löschen von Kommentaren, doch um dem grundlegenden Problem entgegenzuwirken, müssen Ursachen für die Entstehung von Hass im Netz bekämpft werden. In diesem Kapitel wird daher neben Grundlagen der Hatespeechanalyse insbesondere auf Gruppierungen, Informationsfluss und die Ausbreitung von Hass in sozialen Netzwerken eingegangen. Daraus werden dann Maßnahmen zur Bekämpfung der Ursachen von Hass abgeleitet und diskutiert. |
2022 |
| 139. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories Artikel In: Electronics, Bd. 11, Nr. 23, 2022, ISSN: 2079-9292. @article{electronics11233942,
title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories},
author = { Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Kawa Nazemi and Egils Ginters and Michael Bazant},
doi = {10.3390/electronics11233942},
issn = {2079-9292},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
journal = {Electronics},
volume = {11},
number = {23},
abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.},
keywords = {Artificial Intelligence, Case Study, Data Analytics, Data Science, Data Visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions. |
| 138. | Lukas Kaupp; Bernhard Humm; Kawa Nazemi; Stephan Simons Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis Artikel In: Sensors, Bd. 22, Nr. 21, 2022, ISSN: 1424-8220. @article{s22218259,
title = {Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis},
author = {Lukas Kaupp and Bernhard Humm and Kawa Nazemi and Stephan Simons},
doi = {10.3390/s22218259},
issn = {1424-8220},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
journal = {Sensors},
volume = {22},
number = {21},
abstract = {Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.},
keywords = {Artificial Intelligence, Machine Leanring, Machine Learning, smart factory, Smart manufacturing},
pubstate = {published},
tppubtype = {article}
}
Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert. |
