Publikationen
2021 | |
2. | Midhad Blazevic; Lennart B. Sina; Dirk Burkhardt; Melanie Siegel; Kawa Nazemi Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Konferenzbeitrag In: 2021 25th International Conference Information Visualisation (IV), S. 211-217, IEEE, 2021. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Collaboration, Collaborative Systems, Information visualization, Similarity, Visual analytics @inproceedings{9582711, 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 similarity-based 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. |
2010 | |
1. | Cristian Erik Hofmann; Dirk Burkhardt; Matthias Breyer; Kawa Nazemi; Christian Stab; Dieter W. Fellner Towards a Workflow-Based Design of Multimedia Annotation Systems Konferenz Proceedings of ED-Media 2010, The Association for the Advancement of Computing in Education (AACE), 2010, ISBN: 978-1-880094-81-5. Abstract | Links | BibTeX | Schlagwörter: Annotations, Collaboration, Interactive multimedia, Workflow @conference{C35-P-21400, Annotation techniques for multimedia contents have found their way into multiple areas of daily use as well as professional fields. A large number of research projects can be assigned to different specific subareas of digital annotation. Nevertheless, the annotation process, bringing out multiple workflows depending on different application scenarios, has not sufficiently been taken into consideration. A consideration of respective processes and workflows requires detailed knowledge about practices of digital multimedia annotation. In order to establish fundamental groundwork towards workflow-related research, this paper presents a comprehensive process model of multimedia annotation which results from a conducted empirical study. Furthermore, we provide a survey of the tasks that have to be accomplished by users and computing devices, tools and algorithms that are used to handle specific tasks, and types of data that are transferred between workflow steps. These aspects are assigned to the identified sub-processes of the model. |