Scientific Publications

@inproceedings{Sina2020,

title = {Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts},

author = {Lennart Sina and Dirk Burkhardt and Kawa Nazemi},

editor = {Haithem Afli and Udo Bleimann and Dirk Burkhardt and Robert Loew and Stefanie Regier and Ingo Stengel and Haiying Wang and Huiru (Jane) Zheng},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2021/02/2020cerc-ii.pdf, Paper as PDF

http://ceur-ws.org/Vol-2815/CERC2020_paper14.pdf, Paper on CEUR-WS

http://ceur-ws.org/Vol-2815/, Proceedings on CEUR-WS},

issn = {1613-0073},

year  = {2020},

date = {2020-09-11},

booktitle = {Proceedings of the 6th Collaborative European Research Conference (CERC 2020)},

volume = {Vol. 2815},

pages = {222-235},

publisher = {CEUR-WS.org},

address = {Aachen, Germany},

series = {CEUR Workshop Proceedings},

abstract = {The rapid change due to digitalization challenge a variety of market players and force them to find strategies to be aware of developments in these markets, particularly those that impact their business. The main challenge is what a practical solution could look like and how technology can support market players in these trend observation tasks. The paper outlines therefore a technological solution to observe specific authors e.g. researchers who influence a certain market or engineers of competitors. In many branches both are well-known groups to market players and there is almost always the need of a technology that supports the topical observation. This paper focuses on the concept of how a visual dashboard could enable a market observation and how data must be processed for it and its prototypical implementation which enables an evaluation later. Furthermore, the definition of a principal technological analysis for innovation and technology management is created and is also an important contribution to the scientific community that specifically considers the technology perspective and its corresponding requirements.},

note = {urn:nbn:de:0074-2815-0},

keywords = {Business Intelligence, information exploration, Innovation Management, Visual Analytics, Visual Trend Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2019b,

title = {A Mobile Visual Analytics Approach for Instant Trend Analysis in Mobile Contexts},

author = {Dirk Burkhardt and Kawa Nazemi and Arjan Kuijper and Egils Ginters},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2019/09/2019vare-i.pdf, Dowload as PDF},

doi = {  10.5281/zenodo.3473041},

isbn = {978-88-85741-41-6},

year  = {2019},

date = {2019-09-18},

booktitle = {5th International Conference of the Virtual and Augmented Reality in Education (VARE2019)},

pages = {11--19},

publisher = {CAL-TEK SRL},

address = {Rende, Italy},

abstract = {The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design.},

note = {Nominated for Best Paper Award},

keywords = {Business Analytics, Decision Support Systems, Human-Computer Interaction, Information Visualization, Mobile Devices, Mobile Visual Analytics, Visual Trend Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}