Publications ⋆ Kawa Nazemi

Publications


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2021
2.
 Nazemi, Kawa;  Burkhardt, Dirk;  Kock, Alexander
Visual analytics for Technology and Innovation Management: An interaction approach for strategic decisionmaking Journal Article 
In: Multimedia Tools and Applications, vol. 1198, 2021, ISSN: 1573-7721, (Springer Nature).
Abstract | Links | BibTeX | Tags: Emerging Trend Identification, Information visualization, Innovation Management, Interaction Design, Multimedia Interaction, Technology Management, Visual analytics, Visual Trend Analytics
@article{Nazemi2021,

title = {Visual analytics for Technology and Innovation Management: An interaction approach for strategic decisionmaking},

author = {Kawa Nazemi and Dirk Burkhardt and Alexander Kock},

url = {https://link.springer.com/content/pdf/10.1007/s11042-021-10972-3.pdf, Open Access PDF},

doi = {10.1007/s11042-021-10972-3},

issn = {1573-7721},

year  = {2021},

date = {2021-05-20},

urldate = {2021-05-20},

journal = {Multimedia Tools and Applications},

volume = {1198},

abstract = {The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.},

note = {Springer Nature},

keywords = {Emerging Trend Identification, Information visualization, Innovation Management, Interaction Design, Multimedia Interaction, Technology Management, Visual analytics, Visual Trend Analytics},

pubstate = {published},

tppubtype = {article}

}

Close
The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.
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Open Access PDF
doi:10.1007/s11042-021-10972-3
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2020
1.
 Nazemi, Kawa;  Klepsch, Maike J.;  Burkhardt, Dirk;  Kaupp, Lukas
Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing Proceedings Article 
In: 2020 24th International Conference Information Visualisation (IV), pp. 360-367, IEEE Computer Society, 2020, ISSN: 2375-0138.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Data Science, Information Science, Information visualization, Large scale integration, Libraries, Machine Leanring, Market Research, Natural Language Processing, Visual analytics, Visual Trend Analytics
@inproceedings{Nazemi_IV2020,

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},

doi = {10.1109/IV51561.2020.00065},

issn = {2375-0138},

year  = {2020},

date = {2020-09-01},

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

pages = {360-367},

publisher = {IEEE Computer Society},

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 = {Artificial Intelligence, Data Science, Information Science, Information visualization, Large scale integration, Libraries, Machine Leanring, Market Research, Natural Language Processing, Visual analytics, Visual Trend Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
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.
Close
doi:10.1109/IV51561.2020.00065
Close