Publications

Adaptive Information Visualization Adaptive User Interfaces Adaptive Visualization Artificial Intelligence Business Analytics Computer Based Learning cyber-physical systems Data Analytics Data Science Data Visualization E-Learning effectiveness eGovernance Evaluation Exploration Exploratory learning Human Factors Human-centered user interfaces Human-computer interaction (HCI) Information visualization Innovation Management Intelligent Systems Intelligent tutoring systems Interaction analysis Interaction Design Machine Leanring mobility indicators for visual analytics Ontology visualization personalization Policy modeling Process Mining Process Support reference model Semantic data modeling Semantic visualization Semantic web Semantics visualization Simulation Technology Management Trend Analytics User behavior User Interactions User Interface User modeling User-centered design Visual Analytical Reasoning Visual analytics Visual Knowledge Discovery Visual Trend Analysis Visualization

118 entries « ‹ 1 of 5 › »

2022
118.	Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery Book Springer Nature, Cham, 2022, ISBN: 978-3-030-93118-6. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Computational Intelligence, Machine Learning, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery @book{Kovalerchuk2022, title = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery}, editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi}, doi = {10.1007/978-3-030-93119-3}, isbn = {978-3-030-93118-6}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, publisher = {Springer Nature}, address = {Cham}, series = {Studies in Computational Intelligence}, abstract = {This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.}, key = {SP2022}, keywords = {Artificial Intelligence, Computational Intelligence, Machine Learning, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery}, pubstate = {published}, tppubtype = {book} } Close This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes. Close doi:10.1007/978-3-030-93119-3 Close
117.	Kawa Nazemi; Tim Feiter; Lennart B. Sina; Dirk Burkhardt; Alexander Kock Visual Analytics for Strategic Decision Making in Technology Management Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 31–61, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Machine Leanring, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery @inbook{Nazemi2022, title = {Visual Analytics for Strategic Decision Making in Technology Management}, author = {Kawa Nazemi and Tim Feiter and Lennart B. Sina and Dirk Burkhardt and Alexander Kock}, editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi}, url = {https://doi.org/10.1007/978-3-030-93119-3_2}, doi = {10.1007/978-3-030-93119-3_2}, isbn = {978-3-030-93119-3}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery}, pages = {31--61}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Strategic foresight, corporate foresight, and technology management enable firms to detect discontinuous changes early and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning methods and interactive visualizations. It enables a far better way to gather insights from a vast amount of data to make a strategic decision. While Visual Analytics got various models and approaches to enable strategic decision-making, the analysis of trends is still a matter of research. The forecasting approaches and involvement of humans in the visual trend analysis process require further investigation that will lead to sophisticated analytical methods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based on our previous work.}, keywords = {Artificial Intelligence, Machine Leanring, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery}, pubstate = {published}, tppubtype = {inbook} } Close Strategic foresight, corporate foresight, and technology management enable firms to detect discontinuous changes early and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning methods and interactive visualizations. It enables a far better way to gather insights from a vast amount of data to make a strategic decision. While Visual Analytics got various models and approaches to enable strategic decision-making, the analysis of trends is still a matter of research. The forecasting approaches and involvement of humans in the visual trend analysis process require further investigation that will lead to sophisticated analytical methods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based on our previous work. Close https://doi.org/10.1007/978-3-030-93119-3_2 doi:10.1007/978-3-030-93119-3_2 Close
116.	Boris Kovalerchuk; Răzvan Andonie; Nuno Datia; Kawa Nazemi; Ebad Banissi Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 1–27, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract \| Links \| BibTeX \| Tags: @inbook{Kovalerchuk2022b, title = {Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions}, author = {Boris Kovalerchuk and Răzvan Andonie and Nuno Datia and Kawa Nazemi and Ebad Banissi}, editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi}, url = {https://doi.org/10.1007/978-3-030-93119-3_1}, doi = {10.1007/978-3-030-93119-3_1}, isbn = {978-3-030-93119-3}, year = {2022}, date = {2022-01-01}, booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery}, pages = {1--27}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Close Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations. Close https://doi.org/10.1007/978-3-030-93119-3_1 doi:10.1007/978-3-030-93119-3_1 Close
115.	Lukas Kaupp; Kawa Nazemi; Bernhard Humm Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Machine Leanring, Machine Learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery @inbook{Kaupp2022, title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model}, author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm}, editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi}, url = {https://doi.org/10.1007/978-3-030-93119-3_16}, doi = {10.1007/978-3-030-93119-3_16}, isbn = {978-3-030-93119-3}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery}, pages = {403--436}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.}, keywords = {Artificial Intelligence, Machine Leanring, Machine Learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery}, pubstate = {published}, tppubtype = {inbook} } Close The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research. Close https://doi.org/10.1007/978-3-030-93119-3_16 doi:10.1007/978-3-030-93119-3_16 Close
2021
114.	Midhad Blazevic; Lennart B. Sina; Dirk Burkhardt; Melanie Siegel; Kawa Nazemi Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Inproceedings In: 2021 25th International Conference Information Visualisation (IV), pp. 211-217, IEEE, New York, USA, 2021, ISBN: 978-1-6654-3827-8. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Collaborative Systems, Machine Leanring, Similarity, Trend Analytics, Visual analytics, visual business analytics @inproceedings{Blazevic2021, title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data}, author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi}, doi = {10.1109/IV53921.2021.00041}, isbn = {978-1-6654-3827-8}, year = {2021}, date = {2021-10-28}, urldate = {2021-10-28}, booktitle = {2021 25th International Conference Information Visualisation (IV)}, pages = {211-217}, publisher = {IEEE}, address = {New York, USA}, abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similaritybased approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.}, keywords = {Artificial Intelligence, Collaborative Systems, Machine Leanring, Similarity, Trend Analytics, Visual analytics, visual business analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similaritybased approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics. Close doi:10.1109/IV53921.2021.00041 Close
113.	Ebad Banissi; Anna Ursyn; Mark W. McK. Bannatyne; João Moura Pires; Nuno Datia; Mao Lin Huang Weidong Huang; Quang Vinh Nguyen; Kawa Nazemi; Boris Kovalerchuk; Minoru Nakayama; John Counsell; Andrew Agapiou; Farzad Khosrow-shahi; Hing-Wah Chau; Mengbi Li; Richard Laing; Fatma Bouali; Gilles Venturini; Marco Temperini; Muhammad Sarfraz (Ed.) Proceedings of 2021 25th International Conference Information Visualisation (IV) Proceeding IEEE, New York, USA, 2021, ISBN: 978-1-6654-3827-8. Abstract \| Links \| BibTeX \| Tags: Information visualization @proceedings{Banissi2021, title = {Proceedings of 2021 25th International Conference Information Visualisation (IV)}, editor = {Ebad Banissi and Anna Ursyn and Mark W. McK. Bannatyne and João Moura Pires and Nuno Datia and Mao Lin Huang Weidong Huang and Quang Vinh Nguyen and Kawa Nazemi and Boris Kovalerchuk and Minoru Nakayama and John Counsell and Andrew Agapiou and Farzad Khosrow-shahi and Hing-Wah Chau and Mengbi Li and Richard Laing and Fatma Bouali and Gilles Venturini and Marco Temperini and Muhammad Sarfraz}, doi = {10.1109/IV53921.2021.00001}, isbn = {978-1-6654-3827-8}, year = {2021}, date = {2021-10-28}, urldate = {2021-10-28}, booktitle = {Information Visualisation: AI & Analytics, Biomedical Visualization, Builtviz, and Geometric Modelling & Imaging}, pages = {1-775}, publisher = {IEEE}, address = {New York, USA}, abstract = {Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any informationdependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the domain of application and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. 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 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. 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 the processes in all stages of the process, from raw data to the knowledge acquisition stage. This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment. Joining us in this search are some 75 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation.}, keywords = {Information visualization}, pubstate = {published}, tppubtype = {proceedings} } Close Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any informationdependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the domain of application and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. 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 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. 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 the processes in all stages of the process, from raw data to the knowledge acquisition stage. This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment. Joining us in this search are some 75 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation. Close doi:10.1109/IV53921.2021.00001 Close
112.	Kawa Nazemi; Dirk Burkhardt; Alexander Kock Visual analytics for technology and innovation management: An interaction approach for strategic decisionmaking Journal Article In: Multimedia Tools and Applications, 1198 , 2021, ISSN: 1573-7721. Abstract \| Links \| BibTeX \| Tags: emerging trend identification, Information visualization, Innovation Management, Interaction Design, Multimodal Interaction, Technology Management, Visual analytics, Visual Trend Analytics @article{Nazemi2021b, title = {Visual analytics for technology and innovation management: An interaction approach for strategic decisionmaking }, author = {Kawa Nazemi and Dirk Burkhardt and Alexander Kock}, editor = {Rita Francese and Borko Furht}, doi = {10.1007/s11042-021-10972-3}, issn = {1573-7721}, year = {2021}, date = {2021-05-20}, journal = {Multimedia Tools and Applications}, volume = {1198}, abstract = {The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.}, keywords = {emerging trend identification, Information visualization, Innovation Management, Interaction Design, Multimodal Interaction, Technology Management, Visual analytics, Visual Trend Analytics}, pubstate = {published}, tppubtype = {article} } Close The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection. Close doi:10.1007/s11042-021-10972-3 Close
111.	Mina Schütz; Alexander Schindler; Melanie Siegel; Kawa Nazemi Automatic Fake News Detection with Pre-trained Transformer Models Inproceedings In: Alberto Del Bimbo; Rita Cucchiara; Stan Sclaroff; Giovanni Maria Farinella; Tao Mei; Marco Bertini; Hugo Jair Escalante; Roberto Vezzani (Ed.): Pattern Recognition. ICPR International Workshops and Challenges, pp. 627–641, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-68787-8. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Data Analytics, Data Mining, Fake News, maschine learning, Transformer @inproceedings{10.1007/978-3-030-68787-8_45, title = {Automatic Fake News Detection with Pre-trained Transformer Models}, author = {Mina Schütz and Alexander Schindler and Melanie Siegel and Kawa Nazemi}, editor = {Alberto Del Bimbo and Rita Cucchiara and Stan Sclaroff and Giovanni Maria Farinella and Tao Mei and Marco Bertini and Hugo Jair Escalante and Roberto Vezzani}, doi = {10.1007/978-3-030-68787-8_45}, isbn = {978-3-030-68787-8}, year = {2021}, date = {2021-02-21}, booktitle = {Pattern Recognition. ICPR International Workshops and Challenges}, pages = {627--641}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {The automatic detection of disinformation and misinformation has gained attention during the last years, since fake news has a critical impact on democracy, society, and journalism and digital literacy. In this paper, we present a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture. The experiments were conducted on the FakeNewsNet dataset with XLNet, BERT, RoBERTa, DistilBERT, and ALBERT and various combinations of hyperparameters. Different preprocessing steps were carried out with only using the body text, the titles and a concatenation of both. It is concluded that Transformers are a promising approach to detect fake news, since they achieve notable results, even without using a large dataset. Our main contribution is the enhancement of fake news' detection accuracy through different models and parametrizations with a reproducible result examination through the conducted experiments. The evaluation shows that already short texts are enough to attain 85% accuracy on the test set. Using the body text and a concatenation of both reach up to 87% accuracy. Lastly, we show that various preprocessing steps, such as removing outliers, do not have a significant impact on the models prediction output.}, keywords = {Artificial Intelligence, Data Analytics, Data Mining, Fake News, maschine learning, Transformer}, pubstate = {published}, tppubtype = {inproceedings} } Close The automatic detection of disinformation and misinformation has gained attention during the last years, since fake news has a critical impact on democracy, society, and journalism and digital literacy. In this paper, we present a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture. The experiments were conducted on the FakeNewsNet dataset with XLNet, BERT, RoBERTa, DistilBERT, and ALBERT and various combinations of hyperparameters. Different preprocessing steps were carried out with only using the body text, the titles and a concatenation of both. It is concluded that Transformers are a promising approach to detect fake news, since they achieve notable results, even without using a large dataset. Our main contribution is the enhancement of fake news' detection accuracy through different models and parametrizations with a reproducible result examination through the conducted experiments. The evaluation shows that already short texts are enough to attain 85% accuracy on the test set. Using the body text and a concatenation of both reach up to 87% accuracy. Lastly, we show that various preprocessing steps, such as removing outliers, do not have a significant impact on the models prediction output. Close doi:10.1007/978-3-030-68787-8_45 Close
110.	Haithem Afli; Udo Bleimann; Dirk Burkhardt; Robert Loew; Stefanie Regier; Ingo Stengel; Haiying Wang; Huiru (Jane) Zheng (Ed.) Proceedings of the 6th Collaborative European Research Conference (CERC 2020) Proceeding CEUR-WS.org, Aachen, Germany, Vol. 2815 , 2021, ISSN: 1613-0073, (urn:nbn:de:0074-2815-0). Abstract \| Links \| BibTeX \| Tags: Art, Bioinformatics, Biology, Business Information Systems, Civil Engineering, Computer Science, Education, IT Security, Marketing, Multimedia, Psychology @proceedings{CERC2020, title = {Proceedings of the 6th Collaborative European Research Conference (CERC 2020)}, editor = {Haithem Afli and Udo Bleimann and Dirk Burkhardt and Robert Loew and Stefanie Regier and Ingo Stengel and Haiying Wang and Huiru (Jane) Zheng}, url = {http://ceur-ws.org/Vol-2815/, Proceedings on CEUR-WS}, issn = {1613-0073}, year = {2021}, date = {2021-02-17}, booktitle = {CERC2020 Proceedings}, volume = {Vol. 2815}, pages = {433}, publisher = {CEUR-WS.org}, address = {Aachen, Germany}, series = {CEUR Workshop Proceedings}, abstract = {In today's world, which has recently seen fractures and isolation forming among states, internationaland interdisciplinary collaboration is an increasingly important source of progress. Collaboration isa rich source of innovation and growth. It is the goal of the Collaborative European ResearchConference (CERC2020) to foster collaboration among friends and colleagues across disciplinesand nations within Europe. CERC emerged from long-standing cooperation between the CorkInstitute of Technology, Ireland and Hochschule Darmstadt - University of Applied Sciences,Germany. CERC has grown to include more well-established partners in Germany, the UnitedKingdom, Greece, Spain, Italy, and many more. CERC is truly interdisciplinary, bringing together new and experienced researchers from science,engineering, business, humanities, and the arts. At CERC researchers not only present their findingsas published in their research papers. They are also challenged to collaboratively work out jointaspects of their research during conference sessions and informal social events and gatherings. Organizing such an event involves the hard work of many people. COVID-19 pandemic hasimpacted our daily life and research. It has been a significant change to CERC2020 and this is thefirst time the conference was held virtually online. The conference has received submissions fromworldwide, not just European countries. Thanks go to the international program committee and myfellow program chairs, particularly to Prof Udo Bleimann for invaluable support throughout theconference. Prof Ingo Stengel, Dr. Haiying Wang, Dr. Ali Haithem, and Dr. Stefanie Regier forsupporting me in the review process. Dirk Burkhardt and Dr. Robert Loew put a great effort intosetting up the website and conference management system and preparing the conference programmeand proceedings. Thank my colleagues from Ulster University, Hochschule Karlsruhe andHochschule Darmstadt, and the Cork Institute of Technology, Ireland for providing invaluablesupport to the conference. CERC2020 has received supports from Ulster University, VIsit Belfast,and Belfast City Council.}, note = {urn:nbn:de:0074-2815-0}, keywords = {Art, Bioinformatics, Biology, Business Information Systems, Civil Engineering, Computer Science, Education, IT Security, Marketing, Multimedia, Psychology}, pubstate = {published}, tppubtype = {proceedings} } Close In today's world, which has recently seen fractures and isolation forming among states, internationaland interdisciplinary collaboration is an increasingly important source of progress. Collaboration isa rich source of innovation and growth. It is the goal of the Collaborative European ResearchConference (CERC2020) to foster collaboration among friends and colleagues across disciplinesand nations within Europe. CERC emerged from long-standing cooperation between the CorkInstitute of Technology, Ireland and Hochschule Darmstadt - University of Applied Sciences,Germany. CERC has grown to include more well-established partners in Germany, the UnitedKingdom, Greece, Spain, Italy, and many more. CERC is truly interdisciplinary, bringing together new and experienced researchers from science,engineering, business, humanities, and the arts. At CERC researchers not only present their findingsas published in their research papers. They are also challenged to collaboratively work out jointaspects of their research during conference sessions and informal social events and gatherings. Organizing such an event involves the hard work of many people. COVID-19 pandemic hasimpacted our daily life and research. It has been a significant change to CERC2020 and this is thefirst time the conference was held virtually online. The conference has received submissions fromworldwide, not just European countries. Thanks go to the international program committee and myfellow program chairs, particularly to Prof Udo Bleimann for invaluable support throughout theconference. Prof Ingo Stengel, Dr. Haiying Wang, Dr. Ali Haithem, and Dr. Stefanie Regier forsupporting me in the review process. Dirk Burkhardt and Dr. Robert Loew put a great effort intosetting up the website and conference management system and preparing the conference programmeand proceedings. Thank my colleagues from Ulster University, Hochschule Karlsruhe andHochschule Darmstadt, and the Cork Institute of Technology, Ireland for providing invaluablesupport to the conference. CERC2020 has received supports from Ulster University, VIsit Belfast,and Belfast City Council. Close Proceedings on CEUR-WS Close
109.	Lukas Kaupp; Heiko Webert; Kawa Nazemi; Bernhard Humm; Stephan Simons CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory Inproceedings In: Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020), pp. 492-501, Elsevier, 2021, ISSN: 1877-0509. Abstract \| Links \| BibTeX \| Tags: anomaly detection, contextual faults, cyber-physical systems, fault diagnosis, smart factory @inproceedings{Kaupp2021, title = {CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory}, author = {Lukas Kaupp and Heiko Webert and Kawa Nazemi and Bernhard Humm and Stephan Simons}, doi = {10.1016/j.procs.2021.01.265}, issn = {1877-0509}, year = {2021}, date = {2021-02-17}, booktitle = {Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)}, volume = {180}, pages = {492-501}, publisher = {Elsevier}, series = {Procedia Computer Science}, abstract = {Cyber-physical systems in smart factories get more and more integrated and interconnected. Industry 4.0 accelerates this trend even further. Through the broad interconnectivity a new class of faults arise, the contextual faults, where contextual knowledge is needed to find the underlying reason. Fully-automated systems and the production line in a smart factory form a complex environment making the fault diagnosis non-trivial. Along with a dataset, we give a first definition of contextual faults in the smart factory and name initial use cases. Additionally, the dataset encompasses all the data recorded in a current state-of-the-art smart factory. We also add additional information measured by our developed sensing units to enrich the smart factory data even further. In the end, we show a first approach to detect the contextual faults in a manual preliminary analysis of the recorded log data.}, keywords = {anomaly detection, contextual faults, cyber-physical systems, fault diagnosis, smart factory}, pubstate = {published}, tppubtype = {inproceedings} } Close Cyber-physical systems in smart factories get more and more integrated and interconnected. Industry 4.0 accelerates this trend even further. Through the broad interconnectivity a new class of faults arise, the contextual faults, where contextual knowledge is needed to find the underlying reason. Fully-automated systems and the production line in a smart factory form a complex environment making the fault diagnosis non-trivial. Along with a dataset, we give a first definition of contextual faults in the smart factory and name initial use cases. Additionally, the dataset encompasses all the data recorded in a current state-of-the-art smart factory. We also add additional information measured by our developed sensing units to enrich the smart factory data even further. In the end, we show a first approach to detect the contextual faults in a manual preliminary analysis of the recorded log data. Close doi:10.1016/j.procs.2021.01.265 Close
108.	Kawa Nazemi; Lukas Kaupp; Dirk Burkhardt; Nicola Below Datenvisualisierung Book Chapter In: Markus Putnings; Heike Neuroth; Janna Neumann (Ed.): Praxishandbuch Forschungsdatenmanagement, Chapter 5.4, pp. 477-502, De Gruyter, Berlin/Boston, 2021, ISBN: 978-3-11-065365-6. Abstract \| Links \| BibTeX \| Tags: Data Visualization @inbook{Nazemi2021, title = {Datenvisualisierung}, author = {Kawa Nazemi and Lukas Kaupp and Dirk Burkhardt and Nicola Below}, editor = {Markus Putnings and Heike Neuroth and Janna Neumann}, doi = {10.1515/9783110657807-026}, isbn = {978-3-11-065365-6}, year = {2021}, date = {2021-01-18}, booktitle = {Praxishandbuch Forschungsdatenmanagement}, pages = {477-502}, publisher = {De Gruyter}, address = {Berlin/Boston}, chapter = {5.4}, abstract = {Die visuelle Projektion von heterogenen (z. B. Forschungs-)Daten auf einer 2-dimensionalen Fläche, wie etwa einem Bildschirm, wird als Datenvisualisierung bezeichnet. Datenvisualisierung ist ein Oberbegriff für verschiedene Arten der visuellen Projektion. In diesem Kapitel wird zunächst der Begriff definiert und abgegrenzt. Der Fokus des Kapitels liegt auf Informationsvisualisierung und Visual Analytics. In diesem Kontext wird der Prozess der visuellen Transformation vorgestellt. Es soll als Grundlage für eine wissenschaftlich valide Generierung von Visualisierungen dienen, die auch visuelle Aufgaben umfassen. Anwendungsszenarien stellen den Mehrwert der hier vorgestellten Konzepte in der Praxis vor. Der wissenschaftliche Beitrag liegt in einer formalen Definition des visuellen Mappings.}, keywords = {Data Visualization}, pubstate = {published}, tppubtype = {inbook} } Close Die visuelle Projektion von heterogenen (z. B. Forschungs-)Daten auf einer 2-dimensionalen Fläche, wie etwa einem Bildschirm, wird als Datenvisualisierung bezeichnet. Datenvisualisierung ist ein Oberbegriff für verschiedene Arten der visuellen Projektion. In diesem Kapitel wird zunächst der Begriff definiert und abgegrenzt. Der Fokus des Kapitels liegt auf Informationsvisualisierung und Visual Analytics. In diesem Kontext wird der Prozess der visuellen Transformation vorgestellt. Es soll als Grundlage für eine wissenschaftlich valide Generierung von Visualisierungen dienen, die auch visuelle Aufgaben umfassen. Anwendungsszenarien stellen den Mehrwert der hier vorgestellten Konzepte in der Praxis vor. Der wissenschaftliche Beitrag liegt in einer formalen Definition des visuellen Mappings. Close doi:10.1515/9783110657807-026 Close
107.	Midhad Blazevic; Lennart B. Sina; Dirk Burkhardt; Melanie Siegel; Kawa Nazemi Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Inproceedings In: 2021 25th International Conference Information Visualisation (IV), pp. 211-217, IEEE, 2021. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Collaboration, Collaborative Systems, Information visualization, Similarity, Visual analytics @inproceedings{9582711, title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data}, author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi}, url = {https://ieeexplore.ieee.org/document/9582711, IEEE XPlore}, doi = {10.1109/IV53921.2021.00041}, year = {2021}, date = {2021-01-01}, urldate = {2021-01-01}, booktitle = {2021 25th International Conference Information Visualisation (IV)}, pages = {211-217}, publisher = {IEEE}, abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.}, keywords = {Artificial Intelligence, Collaboration, Collaborative Systems, Information visualization, Similarity, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics. Close IEEE XPlore doi:10.1109/IV53921.2021.00041 Close
2020
106.	Dirk Burkhardt; Kawa Nazemi; Egils Ginters Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics Inproceedings In: Janis Grabis; Andrejs Romanovs; Galina Kulesova (Ed.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), pp. 1-6, IEEE, 2020, ISBN: 978-1-7281-9105-8. Abstract \| Links \| BibTeX \| Tags: Adaptive Visualization, logistics, Process Mining, Transportation, Trend Analytics, Visual analytics @inproceedings{Burkhardt2020c, title = {Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics}, author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters}, editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova}, doi = {10.1109/ITMS51158.2020.9259309}, isbn = {978-1-7281-9105-8}, year = {2020}, date = {2020-11-19}, booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)}, pages = {1-6}, publisher = {IEEE}, abstract = {A variety of new technologies and ideas for businesses are arising in the domain of logistics and mobility. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users’ behavior is commonly investigated in adaptive systems, which is considering the induvial preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows generating a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks.}, keywords = {Adaptive Visualization, logistics, Process Mining, Transportation, Trend Analytics, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close A variety of new technologies and ideas for businesses are arising in the domain of logistics and mobility. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users’ behavior is commonly investigated in adaptive systems, which is considering the induvial preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows generating a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks. Close doi:10.1109/ITMS51158.2020.9259309 Close
105.	Artis Aizstrauts; Dirk Burkhardt; Egils Ginters; Kawa Nazemi On Microservice Architecture Based Communication Environment for Cycling Map Developing and Maintenance Simulator Inproceedings In: Janis Grabis; Andrejs Romanovs; Galina Kulesova (Ed.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), pp. 1-4, IEEE, 2020, ISBN: 978-1-7281-9105-8. Abstract \| Links \| BibTeX \| Tags: Easy Communication Environment, microservice architecture, Simulation @inproceedings{Aizstrauts2020c, title = {On Microservice Architecture Based Communication Environment for Cycling Map Developing and Maintenance Simulator}, author = {Artis Aizstrauts and Dirk Burkhardt and Egils Ginters and Kawa Nazemi}, editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova}, doi = {10.1109/ITMS51158.2020.9259299}, isbn = {978-1-7281-9105-8}, year = {2020}, date = {2020-11-19}, booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)}, pages = {1-4}, publisher = {IEEE}, abstract = {Urban transport infrastructure nowadays involves environmentally friendly modes of transport, the most democratic of which is cycling. Citizens will use bicycles if a reasonably designed cycle path scheme will be provided. Cyclists also need to know the characteristics and load of the planned route before the trip. Prediction can be provided by simulation, but it is often necessary to use heterogeneous and distributed models that require a specific communication environment to ensure interaction. The article describes the easy communication environment that is used to provide microservices communication and data exchange in a bicycle route design and maintenance multi-level simulator.}, keywords = {Easy Communication Environment, microservice architecture, Simulation}, pubstate = {published}, tppubtype = {inproceedings} } Close Urban transport infrastructure nowadays involves environmentally friendly modes of transport, the most democratic of which is cycling. Citizens will use bicycles if a reasonably designed cycle path scheme will be provided. Cyclists also need to know the characteristics and load of the planned route before the trip. Prediction can be provided by simulation, but it is often necessary to use heterogeneous and distributed models that require a specific communication environment to ensure interaction. The article describes the easy communication environment that is used to provide microservices communication and data exchange in a bicycle route design and maintenance multi-level simulator. Close doi:10.1109/ITMS51158.2020.9259299 Close
104.	Kawa Nazemi; Matthias Kowald; Till Dannewald; Dirk Burkhardt; Egils Ginters Visual Analytics Indicators for Mobility and Transportation Inproceedings In: Janis Grabis; Andrejs Romanovs; Galina Kulesova (Ed.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), pp. 1-6, IEEE, 2020, ISBN: 978-1-7281-9105-8. Abstract \| Links \| BibTeX \| Tags: mobility analytics, mobility behaviour, mobility indicators for visual analytics, Visual analytics @inproceedings{Nazemi2020c, title = {Visual Analytics Indicators for Mobility and Transportation}, author = {Kawa Nazemi and Matthias Kowald and Till Dannewald and Dirk Burkhardt and Egils Ginters}, editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova}, doi = {10.1109/ITMS51158.2020.9259321}, isbn = {978-1-7281-9105-8}, year = {2020}, date = {2020-11-19}, booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)}, pages = {1-6}, publisher = {IEEE}, abstract = {Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.}, keywords = {mobility analytics, mobility behaviour, mobility indicators for visual analytics, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies. Close doi:10.1109/ITMS51158.2020.9259321 Close
103.	Lennart Sina; Dirk Burkhardt; Kawa Nazemi Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts Inproceedings In: Haithem Afli; Udo Bleimann; Dirk Burkhardt; Robert Loew; Stefanie Regier; Ingo Stengel; Haiying Wang; Huiru (Jane) Zheng (Ed.): Proceedings of the 6th Collaborative European Research Conference (CERC 2020), pp. 222-235, CEUR-WS.org, Aachen, Germany, 2020, ISSN: 1613-0073, (urn:nbn:de:0074-2815-0). Abstract \| Links \| BibTeX \| Tags: business intelligence, information exploration, Innovation Management, Visual analytics, Visual Trend Analysis @inproceedings{Sina2021, title = {Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts}, author = {Lennart Sina and Dirk Burkhardt and Kawa Nazemi}, editor = {Haithem Afli and Udo Bleimann and Dirk Burkhardt and Robert Loew and Stefanie Regier and Ingo Stengel and Haiying Wang and Huiru (Jane) Zheng}, url = {http://ceur-ws.org/Vol-2815/CERC2020_paper14.pdf, Paper on CEUR-WS}, issn = {1613-0073}, year = {2020}, date = {2020-09-11}, booktitle = {Proceedings of the 6th Collaborative European Research Conference (CERC 2020)}, volume = {Vol. 2815}, pages = {222-235}, publisher = {CEUR-WS.org}, address = {Aachen, Germany}, series = {CEUR Workshop Proceedings}, abstract = {The rapid change due to digitalization challenge a variety of market players and force them to find strategies to be aware of developments in these markets, particularly those that impact their business. The main challenge is what a practical solution could look like and how technology can support market players in these trend observation tasks. The paper outlines therefore a technological solution to observe specific authors e.g. researchers who influence a certain market or engineers of competitors. In many branches both are well-known groups to market players and there is almost always the need of a technology that supports the topical observation. This paper focuses on the concept of how a visual dashboard could enable a market observation and how data must be processed for it and its prototypical implementation which enables an evaluation later. Furthermore, the definition of a principal technological analysis for innovation and technology management is created and is also an important contribution to the scientific community that specifically considers the technology perspective and its corresponding requirements.}, note = {urn:nbn:de:0074-2815-0}, keywords = {business intelligence, information exploration, Innovation Management, Visual analytics, Visual Trend Analysis}, pubstate = {published}, tppubtype = {inproceedings} } Close The rapid change due to digitalization challenge a variety of market players and force them to find strategies to be aware of developments in these markets, particularly those that impact their business. The main challenge is what a practical solution could look like and how technology can support market players in these trend observation tasks. The paper outlines therefore a technological solution to observe specific authors e.g. researchers who influence a certain market or engineers of competitors. In many branches both are well-known groups to market players and there is almost always the need of a technology that supports the topical observation. This paper focuses on the concept of how a visual dashboard could enable a market observation and how data must be processed for it and its prototypical implementation which enables an evaluation later. Furthermore, the definition of a principal technological analysis for innovation and technology management is created and is also an important contribution to the scientific community that specifically considers the technology perspective and its corresponding requirements. Close Paper on CEUR-WS Close
102.	Kawa Nazemi; Maike J. Klepsch; Dirk Burkhardt; Lukas Kaupp Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing Inproceedings In: 2020 24th International Conference Information Visualisation (IV), pp. 360-367, IEEE, New York, USA, 2020, ISBN: 978-1-7281-9134-8. Abstract \| Links \| BibTeX \| Tags: Data Science, Natural Language Processing, Visual analytics, Visual Trend Analytics @inproceedings{Nazemi2020d, title = {Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing}, author = {Kawa Nazemi and Maike J. Klepsch and Dirk Burkhardt and Lukas Kaupp}, doi = {10.1109/IV51561.2020.00065}, isbn = {978-1-7281-9134-8}, year = {2020}, date = {2020-09-01}, booktitle = {2020 24th International Conference Information Visualisation (IV)}, pages = {360-367}, publisher = {IEEE}, address = {New York, USA}, abstract = {Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes.}, keywords = {Data Science, Natural Language Processing, Visual analytics, Visual Trend Analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes. Close doi:10.1109/IV51561.2020.00065 Close
101.	Lukas Kaupp; Kawa Nazemi; Bernhard Humm An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing Inproceedings In: 2020 24th International Conference Information Visualisation (IV), pp. 350-359, IEEE, New York, USA, 2020, ISBN: 978-1-7281-9134-8. Abstract \| Links \| BibTeX \| Tags: Analytical models, cyber-physical systems, Data Science, Industries, Outlier Detection, Pipelines, Protocols, Reasoning, Smart manufacturing, Task analysis, Visual analytics @inproceedings{Nazemi2020db, title = {An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing}, author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm}, doi = {10.1109/IV51561.2020.00064}, isbn = {978-1-7281-9134-8}, year = {2020}, date = {2020-09-01}, booktitle = {2020 24th International Conference Information Visualisation (IV)}, pages = {350-359}, publisher = {IEEE}, address = {New York, USA}, abstract = {The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.}, keywords = {Analytical models, cyber-physical systems, Data Science, Industries, Outlier Detection, Pipelines, Protocols, Reasoning, Smart manufacturing, Task analysis, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing. Close doi:10.1109/IV51561.2020.00064 Close
100.	Ebad Banissi; Farzad Khosrow-shahi; Anna Ursyn; Mark W. McK. Bannatyne; João Moura Pires; Nuno Datia; Kawa Nazemi; Boris Kovalerchuk; John Counsell; Andrew Agapiou; Zora Vrcelj; Hing-Wah Chau; Mengbi Li; Gehan Nagy; Richard Laing; Rita Francese; Muhammad Sarfraz; Fatma Bouali; Gilles Venturin; Marjan Trutschl; Urska Cvek; Heimo Müller; Minoru Nakayama; Marco Temperini; Tania Di Mascio; Filippo SciarroneVeronica Rossano; Ralf Dörner; Loredana Caruccio; Autilia Vitiello; Weidong Huang; Michele Risi; Ugo Erra; Razvan Andonie; Muhammad Aurangzeb Ahmad; Ana Figueiras; and Mabule Samuel Mabakane (Ed.) Proceedings of 2020 24th International Conference Information Visualisation (IV) Proceeding IEEE, New York, USA, 2020, ISBN: 978-1-7281-9134-8. Abstract \| Links \| BibTeX \| Tags: Information visualization @proceedings{Banissi2020, title = {Proceedings of 2020 24th International Conference Information Visualisation (IV)}, editor = {Ebad Banissi and Farzad Khosrow-shahi and Anna Ursyn and Mark W. McK. Bannatyne and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and John Counsell and Andrew Agapiou and Zora Vrcelj and Hing-Wah Chau and Mengbi Li and Gehan Nagy and Richard Laing and Rita Francese and Muhammad Sarfraz and Fatma Bouali and Gilles Venturin and Marjan Trutschl and Urska Cvek and Heimo Müller and Minoru Nakayama and Marco Temperini and Tania Di Mascio and Filippo SciarroneVeronica Rossano and Ralf Dörner and Loredana Caruccio and Autilia Vitiello and Weidong Huang and Michele Risi and Ugo Erra and Razvan Andonie and Muhammad Aurangzeb Ahmad and Ana Figueiras and and Mabule Samuel Mabakane}, doi = {10.1109/IV51561.2020}, isbn = {978-1-7281-9134-8}, year = {2020}, date = {2020-09-01}, booktitle = {Information Visualisation: AI & Analytics, Biomedical Visualization, Builtviz, and Geometric Modelling & Imaging}, pages = {1-775}, publisher = {IEEE}, address = {New York, USA}, abstract = {In the current information era, most aspects of life depend on and are driven by data, information, knowledge, user experience, and cultural influences. The infrastructure of any information-dependent society relies on the quality of data, information and analysis of such entities for short to long term as well as past and future activities. Information Visualisation, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science, an aspect that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition such that its dependencies on the domain of application and its evolution steer the next generation of research activities. Processing the relationship between these phases, from the raw data to knowledge, has added new impetus to the way these are understood and communicated. The tradition of use and communication by visualisation is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience and leads to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualisation have added momentum in developing tools that exploit 2D and 3D metaphor-driven techniques within many applied domains. The techniques are developed beyond visualisation to simplify the complexities, to reveal ambiguity, and to work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; how this uncertainty is built into the processes that exist in all stages of the process, from raw data to the knowledge acquisition stage. This collection of papers on this year's information visualisation forum, compiled for the 24th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine- & deeplearning - Biomedical Visualization, Learning Analytics & Geometric Modelling and Imaging - IV2020, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2020 provides the opportunity to resonate with many international and collaborative research projects as well as lectures from distinguished speakers that channels the way this new framework conceptually, as well as practically has been realised. This year's theme is enhanced further by AI, Social Networks impact on social, cultural and heritage aspect of life and learning analysis of today's multifaceted and data-rich environment. Joining us in this search are some 100 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualisation, analytics, applications, and results of the work of researchers, artists and professionals from more than 25 countries. It has allowed us to address the scope of visualisation from a much broader perspective. Each contributor to this conference has indeed added fresh perspectives and thoughts, challenges our beliefs and encouraged further our adventure of innovation.}, keywords = {Information visualization}, pubstate = {published}, tppubtype = {proceedings} } Close In the current information era, most aspects of life depend on and are driven by data, information, knowledge, user experience, and cultural influences. The infrastructure of any information-dependent society relies on the quality of data, information and analysis of such entities for short to long term as well as past and future activities. Information Visualisation, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science, an aspect that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition such that its dependencies on the domain of application and its evolution steer the next generation of research activities. Processing the relationship between these phases, from the raw data to knowledge, has added new impetus to the way these are understood and communicated. The tradition of use and communication by visualisation is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience and leads to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualisation have added momentum in developing tools that exploit 2D and 3D metaphor-driven techniques within many applied domains. The techniques are developed beyond visualisation to simplify the complexities, to reveal ambiguity, and to work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; how this uncertainty is built into the processes that exist in all stages of the process, from raw data to the knowledge acquisition stage. This collection of papers on this year's information visualisation forum, compiled for the 24th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine- & deeplearning - Biomedical Visualization, Learning Analytics & Geometric Modelling and Imaging - IV2020, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2020 provides the opportunity to resonate with many international and collaborative research projects as well as lectures from distinguished speakers that channels the way this new framework conceptually, as well as practically has been realised. This year's theme is enhanced further by AI, Social Networks impact on social, cultural and heritage aspect of life and learning analysis of today's multifaceted and data-rich environment. Joining us in this search are some 100 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualisation, analytics, applications, and results of the work of researchers, artists and professionals from more than 25 countries. It has allowed us to address the scope of visualisation from a much broader perspective. Each contributor to this conference has indeed added fresh perspectives and thoughts, challenges our beliefs and encouraged further our adventure of innovation. Close doi:10.1109/IV51561.2020 Close
99.	Artis Aizstrauts; Egils Ginters; Dirk Burkhardt; Kawa Nazemi Bicycle Path Network Designing and Exploitation Simulation as a Microservice Architecture Inproceedings In: Egils Ginters; Mario Arturo Ruiz Estrada; Miquel Angel Piera Eroles (Ed.): ICTE in Transportation and Logistics 2019, pp. 344–351, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-39688-6. Abstract \| Links \| BibTeX \| Tags: Bicycle path network planning, Easy Communication Environment, Sociotechnical systems simulation @inproceedings{Aizstrauts2020, title = {Bicycle Path Network Designing and Exploitation Simulation as a Microservice Architecture}, author = {Artis Aizstrauts and Egils Ginters and Dirk Burkhardt and Kawa Nazemi}, editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles}, doi = {10.1007/978-3-030-39688-6_43}, isbn = {978-3-030-39688-6}, year = {2020}, date = {2020-01-31}, booktitle = {ICTE in Transportation and Logistics 2019}, pages = {344--351}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Simulation is recognized as a suitable tool for sociotechnical systems research. But the variety and complexity of sociotechnical systems often leads to the need for distributed simulation solutions to understand them. Models that are built for infrastructure planning are typical examples. They combine different domains and involve variety of simulation approaches. This article proposes an easy management environment that is used for VeloRouter software -- a multi agent-based bicycle path network and exploitation simulator that is built as a microservice architecture where each domain simulation is executed as a different microservice.}, keywords = {Bicycle path network planning, Easy Communication Environment, Sociotechnical systems simulation}, pubstate = {published}, tppubtype = {inproceedings} } Close Simulation is recognized as a suitable tool for sociotechnical systems research. But the variety and complexity of sociotechnical systems often leads to the need for distributed simulation solutions to understand them. Models that are built for infrastructure planning are typical examples. They combine different domains and involve variety of simulation approaches. This article proposes an easy management environment that is used for VeloRouter software -- a multi agent-based bicycle path network and exploitation simulator that is built as a microservice architecture where each domain simulation is executed as a different microservice. Close doi:10.1007/978-3-030-39688-6_43 Close
98.	Kawa Nazemi; Dirk Burkhardt; Lukas Kaupp; Till Dannewald; Matthias Kowald; Egils Ginters Visual Analytics in Mobility, Transportation and Logistics Inproceedings In: Egils Ginters; Mario Arturo Ruiz Estrada; Miquel Angel Piera Eroles (Ed.): ICTE in Transportation and Logistics 2019, pp. 82–89, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-39688-6. Abstract \| Links \| BibTeX \| Tags: Data Analytics, Mobility Behavior, Visual analytics @inproceedings{Nazemi2020, title = {Visual Analytics in Mobility, Transportation and Logistics}, author = {Kawa Nazemi and Dirk Burkhardt and Lukas Kaupp and Till Dannewald and Matthias Kowald and Egils Ginters}, editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles}, doi = {10.1007/978-3-030-39688-6_12}, isbn = {978-3-030-39688-6}, year = {2020}, date = {2020-01-01}, booktitle = {ICTE in Transportation and Logistics 2019}, pages = {82--89}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans' mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. New technologies will lead to a different mobility behavior with new constraints. These changes in mobility behavior and logistics require analytical systems to forecast the required information and probably appearing changes. These systems have to consider different perspectives and employ multiple indicators. Visual Analytics provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary case studies to illustrate the advantages of such systems. The examples are aimed to demonstrate the benefits of Visual Analytics in mobility.}, keywords = {Data Analytics, Mobility Behavior, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans' mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. New technologies will lead to a different mobility behavior with new constraints. These changes in mobility behavior and logistics require analytical systems to forecast the required information and probably appearing changes. These systems have to consider different perspectives and employ multiple indicators. Visual Analytics provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary case studies to illustrate the advantages of such systems. The examples are aimed to demonstrate the benefits of Visual Analytics in mobility. Close doi:10.1007/978-3-030-39688-6_12 Close
97.	Dirk Burkhardt; Kawa Nazemi; Egils Ginters Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations Inproceedings In: Egils Ginters; Mario Arturo Ruiz Estrada; Miquel Angel Piera Eroles (Ed.): ICTE in Transportation and Logistics 2019, pp. 319–327, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-39688-6. Abstract \| Links \| BibTeX \| Tags: Adaptive Visualization, Process Mining, Transportation and Logistics @inproceedings{Burkhardt2020, title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations}, author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters}, editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles}, doi = {10.1007/978-3-030-39688-6_40}, isbn = {978-3-030-39688-6}, year = {2020}, date = {2020-01-01}, booktitle = {ICTE in Transportation and Logistics 2019}, pages = {319--327}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.}, keywords = {Adaptive Visualization, Process Mining, Transportation and Logistics}, pubstate = {published}, tppubtype = {inproceedings} } Close In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks. Close doi:10.1007/978-3-030-39688-6_40 Close
2019
96.	Kawa Nazemi; Dirk Burkhardt Advanced Visual Analytical Reasoning for Technology and Innovation Management (AVARTIM) Miscellaneous Forschungstag 2019 der Hessischen Hochschulen für Angewandte Wissenschaften (HAW), Frankfurt, Germany, 2019. Abstract \| Links \| BibTeX \| Tags: Innovation Management, Technology Management, Trend Analytics, Visual Analytical Reasoning, Visual analytics @misc{Nazemi2019db, title = {Advanced Visual Analytical Reasoning for Technology and Innovation Management (AVARTIM)}, author = {Kawa Nazemi and Dirk Burkhardt}, url = {https://www.hessen.de/presse/veranstaltung/forschungstag-2019-der-hessischen-hochschulen-fuer-angewandte-wissenschaften, Event Website}, doi = {10.5281/zenodo.3517296}, year = {2019}, date = {2019-10-29}, abstract = {Im Rahmen des Vorhabens soll mit „AVARTIM“ ein softwaregestützter Prozess zum Erkennen und Bewerten von Trends, Markt- und Technologiesignalen entwickelt werden, um den Prozess des Innovations- und Technologiemanagements nachhaltig zu unterstützen. Dabei soll im Rahmen des Vorhabens eine Infrastruktur an der Hochschule Darmstadt aufgebaut werden, die modular ist und somit auf technologische Veränderungen schnell reagieren kann. Die zu entwickelnde Infrastruktur dient hierbei als Vorlaufforschung und Ausgangstechnologie sowohl für den industriellen Einsatz durch und mit den KMU Partnern als auch zur Beantragung von Verbundvorhaben.}, howpublished = {Forschungstag 2019 der Hessischen Hochschulen für Angewandte Wissenschaften (HAW), Frankfurt, Germany}, keywords = {Innovation Management, Technology Management, Trend Analytics, Visual Analytical Reasoning, Visual analytics}, pubstate = {published}, tppubtype = {misc} } Close Im Rahmen des Vorhabens soll mit „AVARTIM“ ein softwaregestützter Prozess zum Erkennen und Bewerten von Trends, Markt- und Technologiesignalen entwickelt werden, um den Prozess des Innovations- und Technologiemanagements nachhaltig zu unterstützen. Dabei soll im Rahmen des Vorhabens eine Infrastruktur an der Hochschule Darmstadt aufgebaut werden, die modular ist und somit auf technologische Veränderungen schnell reagieren kann. Die zu entwickelnde Infrastruktur dient hierbei als Vorlaufforschung und Ausgangstechnologie sowohl für den industriellen Einsatz durch und mit den KMU Partnern als auch zur Beantragung von Verbundvorhaben. Close Event Website doi:10.5281/zenodo.3517296 Close
95.	Kawa Nazemi; Dirk Burkhardt A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Daniela Ushizima; Sek Chai; Shinjiro Sueda; Xin Lin; Aidong Lu; Daniel Thalmann; Chaoli Wang; Panpan Xu (Ed.): Advances in Visual Computing, pp. 283–294, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-33723-0. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, maschine learning, Visual analytics @inproceedings{Nazemi_ISVC2019, title = {A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management}, author = {Kawa Nazemi and Dirk Burkhardt}, editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu}, url = {https://rd.springer.com/chapter/10.1007/978-3-030-33723-0_23, Springer LNCS https://dx.doi.org/10.5281/zenodo.3473065, doi:10.5281/zenodo.3473065 (Poster)}, doi = {10.1007/978-3-030-33723-0_23}, isbn = {978-3-030-33723-0}, year = {2019}, date = {2019-10-09}, booktitle = {Advances in Visual Computing}, pages = {283--294}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.}, keywords = {Artificial Intelligence, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, maschine learning, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics. Close Springer LNCS doi:10.5281/zenodo.3473065 (Poster) doi:10.1007/978-3-030-33723-0_23 Close
94.	Dirk Burkhardt; Kawa Nazemi; Arjan Kuijper; Egils Ginters A Mobile Visual Analytics Approach for Instant Trend Analysis in Mobile Contexts Inproceedings In: 5th International Conference of the Virtual and Augmented Reality in Education (VARE2019), pp. 11–19, CAL-TEK SRL, Rende, Italy, 2019, ISBN: 978-88-85741-41-6, (Nominated for Best Paper Award). Abstract \| Links \| BibTeX \| Tags: Business Analytics, Decision Support Systems, Human-Computer Interaction, Information visualization, Mobile Devices, Mobile Visual Analytics, Visual Trend Analysis @inproceedings{Burkhardt2019b, title = {A Mobile Visual Analytics Approach for Instant Trend Analysis in Mobile Contexts}, author = {Dirk Burkhardt and Kawa Nazemi and Arjan Kuijper and Egils Ginters}, doi = {10.5281/zenodo.3473041}, isbn = {978-88-85741-41-6}, year = {2019}, date = {2019-09-18}, booktitle = {5th International Conference of the Virtual and Augmented Reality in Education (VARE2019)}, pages = {11--19}, publisher = {CAL-TEK SRL}, address = {Rende, Italy}, abstract = {The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design.}, note = {Nominated for Best Paper Award}, keywords = {Business Analytics, Decision Support Systems, Human-Computer Interaction, Information visualization, Mobile Devices, Mobile Visual Analytics, Visual Trend Analysis}, pubstate = {published}, tppubtype = {inproceedings} } Close The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design. Close doi:10.5281/zenodo.3473041 Close

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