Scientific Publications

2021
3.		Nazemi, Kawa; Burkhardt, Dirk; Kock, Alexander Visual analytics for technology and innovation management: An interaction approach for strategic decisionmaking Journal Article In: Multimedia Tools and Applications, vol. 1198, 2021, ISSN: 1380-7501. Abstract \| Links \| BibTeX \| Tags: Emerging Trend Identification, Information Visualization, Innovation Management, Interaction Design, Multimedia 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}, url = {https://dirk.burkhardt.xyz/wp-content/uploads/2021/05/2021mtap.pdf, Paper as PDF}, doi = {10.1007/s11042-021-10972-3}, issn = {1380-7501}, year = {2021}, date = {2021-05-20}, urldate = {2021-05-20}, journal = {Multimedia Tools and Applications}, volume = {1198}, publisher = {Springer}, 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, Multimedia 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 Paper as PDF doi:10.1007/s11042-021-10972-3 Close
2019
2.		Nazemi, Kawa; Burkhardt, Dirk 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{Nazemi2019e, title = {Advanced Visual Analytical Reasoning for Technology and Innovation Management (AVARTIM)}, author = {Kawa Nazemi and Dirk Burkhardt}, url = {https://dirk.burkhardt.xyz/wp-content/uploads/2019/11/2019forschungstag.pdf, Poster as PDF 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 Poster as PDF Event Website doi:10.5281/zenodo.3517296 Close
1.		Nazemi, Kawa; Burkhardt, Dirk Visual Text Analytics for Technology and Innovation Management Miscellaneous Presented at OpenRheinMain Conference (ORM2019), 13 September 2019, Darmstadt, Germany, 2019. Abstract \| Links \| BibTeX \| Tags: Business Analytics, Innovation Management, Technology Management, Text Analysis, Trend Analytics, Visual Text Analytics @misc{Nazemi2019b, title = {Visual Text Analytics for Technology and Innovation Management}, author = {Kawa Nazemi and Dirk Burkhardt}, url = {https://dirk.burkhardt.xyz/wp-content/uploads/2019/09/2019orm.pdf, Paper as PDF https://www.openrheinmain.org/2019/presentations/visual_text_analytics_for_technology_and_innovation_management.pdf, Presentation as PDF}, doi = {10.5281/zenodo.3408391}, year = {2019}, date = {2019-09-13}, abstract = {Through coupling of Data Mining, Visual Analytics and Business Analytics techniques, we created a novel solution for strategic market analysis with focus on early trend recognition. As fundament, we are able to consider a variety of text data, as for instance research publications available from a number of (open access) digital libraries, reports and other data from companies, web data about markets as well as news from companies or social media data etc. In an advanced and unified processing pipeline, the information is extracted and mined for a variety of analytical purposes. Via an interactive analysis user-interface, domain experts are able to analysis strong and weak signals in perspective of upcoming trends.}, howpublished = {Presented at OpenRheinMain Conference (ORM2019), 13 September 2019, Darmstadt, Germany}, keywords = {Business Analytics, Innovation Management, Technology Management, Text Analysis, Trend Analytics, Visual Text Analytics}, pubstate = {published}, tppubtype = {misc} } Close Through coupling of Data Mining, Visual Analytics and Business Analytics techniques, we created a novel solution for strategic market analysis with focus on early trend recognition. As fundament, we are able to consider a variety of text data, as for instance research publications available from a number of (open access) digital libraries, reports and other data from companies, web data about markets as well as news from companies or social media data etc. In an advanced and unified processing pipeline, the information is extracted and mined for a variety of analytical purposes. Via an interactive analysis user-interface, domain experts are able to analysis strong and weak signals in perspective of upcoming trends. Close Paper as PDF Presentation as PDF doi:10.5281/zenodo.3408391 Close