@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},

doi = {10.1109/IV53921.2021.00041},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

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, Collaborative Systems, Information visualization, Similarity, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}