@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}

}