Publications ⋆ Kawa Nazemi

Publications


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2021
2.
 Blazevic, Midhad;  Sina, Lennart B.;  Burkhardt, Dirk;  Siegel, Melanie;  Nazemi, Kawa
Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Proceedings Article 
In: 2021 25th International Conference Information Visualisation (IV), pp. 211-217, IEEE , 2021.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics
@inproceedings{9582711,

title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data},

author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi},

url = {https://ieeexplore.ieee.org/document/9582711, IEEE Xplore},

doi = {10.1109/IV53921.2021.00041},

year  = {2021},

date = {2021-03-01},

urldate = {2021-03-01},

booktitle = {2021 25th International Conference Information Visualisation (IV)},

pages = {211-217},

publisher = {IEEE },

abstract = {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.},

keywords = {Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
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.
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IEEE Xplore
doi:10.1109/IV53921.2021.00041
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2010
1.
 Hofmann, Cristian Erik;  Burkhardt, Dirk;  Breyer, Matthias;  Nazemi, Kawa;  Stab, Christian;  Fellner, Dieter W
Towards a Workflow-Based Design of Multimedia Annotation Systems Conference 
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 | Tags: Annotations, Collaboration, Interactive multimedia, Workflow
@conference{C35-P-21400,

title = {Towards a Workflow-Based Design of Multimedia Annotation Systems},

author = {Cristian Erik Hofmann and Dirk Burkhardt and Matthias Breyer and Kawa Nazemi and Christian Stab and Dieter W Fellner},

editor = {J. Herrington and C. Montgomerie},

url = {https://www.learntechlib.org/p/34788/},

isbn = {978-1-880094-81-5},

year  = {2010},

date = {2010-01-01},

booktitle = {Proceedings of ED-Media 2010},

pages = {1224-1233},

publisher = {The Association for the Advancement of Computing in Education (AACE)},

abstract = {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.},

keywords = {Annotations, Collaboration, Interactive multimedia, Workflow},

pubstate = {published},

tppubtype = {conference}

}

Close
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.
Close
https://www.learntechlib.org/p/34788/
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