2020 |
| 5. | Dirk Burkhardt; Kawa Nazemi; Egils Ginters Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics Konferenzbeitrag In: Janis Grabis; Andrejs Romanovs; Galina Kulesova (Hrsg.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), S. 1-6, IEEE, 2020, ISBN: 978-1-7281-9105-8. @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}
}
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. |
2019 |
| 4. | Kawa Nazemi; Dirk Burkhardt Advanced Visual Analytical Reasoning for Technology and Innovation Management (AVARTIM) Sonstige Forschungstag 2019 der Hessischen Hochschulen für Angewandte Wissenschaften (HAW), Frankfurt, Germany, 2019. @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}
}
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. |
| 3. | Kawa Nazemi; Dirk Burkhardt Visual Text Analytics for Technology and Innovation Management Sonstige Presented at OpenRheinMain Conference (ORM2019), 13 September 2019, Darmstadt, Germany, 2019. @misc{Nazemi2019c,
title = {Visual Text Analytics for Technology and Innovation Management},
author = {Kawa Nazemi and Dirk Burkhardt},
url = {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}
}
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. |
| 2. | Kawa Nazemi; Dirk Burkhardt Visual Analytics for Analyzing Technological Trends from Text Konferenzbeitrag In: 2019 23rd International Conference Information Visualisation (IV), S. 191-200, IEEE, 2019, ISSN: 2375-0138, (Best Paper Award). @inproceedings{Nazemi2019d,
title = {Visual Analytics for Analyzing Technological Trends from Text},
author = {Kawa Nazemi and Dirk Burkhardt},
doi = {10.1109/IV.2019.00041},
issn = {2375-0138},
year = {2019},
date = {2019-07-01},
urldate = {2019-07-01},
booktitle = {2019 23rd International Conference Information Visualisation (IV)},
pages = {191-200},
publisher = {IEEE},
abstract = {The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends.},
note = {Best Paper Award},
keywords = {Artificial Intelligence, Data Mining, Data Models, Data Visualization, emerging trend identification, Hidden Markov models, Information visualization, Market research, Patents, Trend Analytics, Visual analytics, visual business analytics, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends. |
| 1. | Kawa Nazemi Visual Trend Analytics in Digital Libraries Sonstige Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval., 2019. @misc{Naz19ASIST,
title = {Visual Trend Analytics in Digital Libraries},
author = {Kawa Nazemi},
url = {https://zenodo.org/record/3264801#.XSBcMo_gpaR, Zenodo Open Access},
doi = {10.5281/zenodo.3264801},
year = {2019},
date = {2019-04-26},
abstract = {The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies’ reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3].},
howpublished = {Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval.},
keywords = {Information visualization, Trend analysis, Trend Analytics, Visual analytics},
pubstate = {published},
tppubtype = {misc}
}
The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies’ reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3]. |
