Scientific Publications

@inproceedings{Blazevic2021,

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://dirk.burkhardt.xyz/wp-content/uploads/2021/11/2021iV.pdf, Paper as PDF},

doi = {10.1109/IV53921.2021.00041},

isbn = {978-1-6654-3827-8},

year  = {2021},

date = {2021-10-28},

urldate = {2021-10-28},

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

pages = {211-217},

publisher = {IEEE},

address = {New York, USA},

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 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.},

keywords = {Collaborative Systems, iV, Similarity, Trend Analytics, Visual Analytics, Visual Business Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2020c,

title = {Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing},

author = {Kawa Nazemi and Maike J. Klepsch and Dirk Burkhardt and Lukas Kaupp},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2021/03/2020iv.pdf, Paper as PDF},

doi = {10.1109/IV51561.2020.00065},

isbn = {978-1-7281-9134-8},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

booktitle = {2020 24th International Conference Information Visualisation (IV)},

pages = {360-367},

publisher = {IEEE},

address = {New York, USA},

abstract = {Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes.},

keywords = {Data Science, iV, Natural Language Processing, Visual Analytics, Visual Trend 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},

urldate = {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, iV, Trend Analytics, Visual Analytics, Visual Business Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}