Scientific Publications

@inproceedings{Burkhardt2020b,
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},
url = {https://dirk.burkhardt.xyz/wp-content/uploads/2020/11/2020itms-ii.pdf, Paper as PDF},
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}
}

@inproceedings{Nazemi2018b,
title = {Visual Analytics for Analyzing Technological Trends from Text},
author = {Kawa Nazemi and Dirk Burkhardt},
url = {https://dirk.burkhardt.xyz/wp-content/uploads/2019/08/2019iV.pdf, Paper as PDF},
doi = {10.1109/IV.2019.00041},
issn = {2375-0138},
year = {2019},
date = {2019-07-02},
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 = {Emerging Trend Identification, Information Visualization, Trend Analytics, Visual Analytics, Visual Business Analytics},
pubstate = {published},
tppubtype = {inproceedings}
}