Blazevic, Midhad; Sina, Lennart B.; Burkhardt, Dirk; Siegel, Melanie; Nazemi, Kawa
In: 2021 25th International Conference Information Visualisation (IV), pp. 211-217, IEEE, New York, USA, 2021, ISBN: 978-1-6654-3827-8.
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.
Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils
In: Grabis, Janis; Romanovs, Andrejs; Kulesova, Galina (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.
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.
Below, Nicola; Burkhardt, Dirk; Kaupp, Lukas; Nazemi, Kawa
In: Afli, Haithem; Bleimann, Udo; Burkhardt, Dirk; Loew, Robert; Regier, Stefanie; Stengel, Ingo; Wang, Haiying; Zheng, Huiru (Jane) (Ed.): Proceedings of the 6th Collaborative European Research Conference (CERC 2020), pp. 414-426, CEUR-WS.org, Aachen, Germany, 2020, ISSN: 1613-0073, (urn:nbn:de:0074-2815-0).
Analytical Reasoning by applying machine learning approaches, artificial intelligence, NLP and visualizations allow to get deep insights into the different domains of various stakeholders and enable to solve complex tasks. Thereby the tasks are very heterogenous and subject of investigation in the different areas of application. These tasks or challenges should be defined by the stakeholders themselves and lead through a deep investigation to advanced analytical approaches. We therefore set up a strategic alliance of research, enterprises and societal organization with the goal of a strong collaboration to identify in a first step these challenges and workout technological solutions for each application scenario. We give in this paper a first draft of current challenges and technological advancements. The main contribution of this paper is next to an accurate description of the current challenges in the analytics domain, also the description of an agenda how these challenges can be solved. Furthermore, a process is explained, how the strategic alliance should act and organize their work to realize beneficial and useful analytical solutions.
Nazemi, Kawa; Burkhardt, Dirk
Forschungstag 2019 der Hessischen Hochschulen für Angewandte Wissenschaften (HAW), Frankfurt, Germany, 2019.
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.
Nazemi, Kawa; Burkhardt, Dirk
Presented at OpenRheinMain Conference (ORM2019), 13 September 2019, Darmstadt, Germany, 2019.
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.
Nazemi, Kawa; Burkhardt, Dirk
In: 2019 23rd International Conference Information Visualisation (iV), pp. 191-200, IEEE, 2019, ISSN: 2375-0138, (Best Paper Award).
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.
Dipl.-Inf. Dirk Burkhardt
Max-Planck-Str. 2, 64807 Dieburg
Building F16 / Office 1.21