@inproceedings{Nazemi2019d,

title = {Visual Analytics for Analyzing Technological Trends from Text},

author = {Kawa Nazemi and Dirk Burkhardt},

doi = {10.1109/IV.2019.00041},

issn = {2375-0138},

year  = {2019},

date = {2019-07-01},

urldate = {2019-07-01},

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 = {Artificial Intelligence, Data Mining, Data Models, Data Visualization, emerging trend identification, Hidden Markov models, Information visualization, Market research, Patents, Trend Analytics, Visual analytics, visual business analytics, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Breyer2012c,

title = {Visualizations Encourage Uncertain Users to High Effectiveness},

author = {Matthias Breyer and Jana Birkenbusch and Dirk Burkhardt and Christopher Schwarz and Christian Stab and Kawa Nazemi and Oliver Christ},

editor = {Gavriel Salvendy and Waldemar Karwowski},

url = {https://www.crcpress.com/Advances-in-Human-Factors-and-Ergonomics-2012--14-Volume-Set-Proceedings/Salvendy-Karwowski/p/book/9781466552623, DigLib Site},

isbn = {9781466552623},

year  = {2012},

date = {2012-08-12},

booktitle = {Advances in Human Factors and Ergonomics 2012- 14 Volume Set: Proceedings of the 4th AHFE Conference 21-25 July 2012},

pages = {8066--8074},

publisher = {CRC Press},

address = {Boca Raton, FL, USA},

organization = {Taylor & Francis},

abstract = {Users have to handle a lot of information in order to fulfill their current task. For achieving an appropriate time and level of quality the users’ motivation plays a key role. In this paper we present a user study which aimed to evaluate if the self-rated expertise of the subjects in their computer system skills has an impact on their task completion effectiveness using visualizations. The results reveal that regardless of the self-rated assurance of the users, no significant difference in the effectiveness of task completions using visualizations could be registered. Furthermore the participants indicate in the questionnaire that using visualization their individual satisfaction level had no significant differences when compared to the users’ self-assurance levels. This indicates even users feeling not confident in interacting with computer systems they may feel confident in interacting with visualizations. Thus if visualizations are applied for tasks of information search and exploration, the user is encouraged to higher effectiveness.},

keywords = {effectiveness, Evaluation, Satisfaction, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}