Scientific Publications

2022
2.		Nazemi, Kawa; Feiter, Tim; Sina, Lennart B.; Burkhardt, Dirk; Kock, Alexander Visual Analytics for Strategic Decision Making in Technology Management Book Chapter In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, Chapter 1, 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}, doi = {10.1007/978-3-030-93119-3_2}, isbn = {978-3-030-93119-3}, year = {2022}, date = {2022-06-05}, urldate = {2022-06-22}, booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery}, pages = {31--61}, publisher = {Springer International Publishing}, address = {Cham}, chapter = {1}, 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 doi:10.1007/978-3-030-93119-3_2 Close
2019
1.		Nazemi, Kawa; Burkhardt, Dirk A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Ushizima, Daniela; Chai, Sek; Sueda, Shinjiro; Lin, Xin; Lu, Aidong; Thalmann, Daniel; Wang, Chaoli; Xu, Panpan (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 Interfaces, Human-Computer Interaction, Information Visualization, Intelligent Systems, maschine learning, Visual Analytics @inproceedings{Nazemi2019c, 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://dirk.burkhardt.xyz/wp-content/uploads/2019/10/2019isvc.pdf, Paper as PDF 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 Interfaces, Human-Computer Interaction, 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 Paper as PDF doi:10.5281/zenodo.3473065 (Poster) doi:10.1007/978-3-030-33723-0_23 Close