Publications ⋆ Kawa Nazemi

Publications


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2022
2.
 Kaupp, Lukas;  Nazemi, Kawa;  Humm, Bernhard
Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories Journal Article 
In: Electronics, vol. 11, no. 23, 2022, ISSN: 2079-9292.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics
@article{electronics11233942,

title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories},

author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},

editor = {Kawa Nazemi and Egils Ginters and Michael Bazant},

url = {https://www.mdpi.com/2079-9292/11/23/3942},

doi = {10.3390/electronics11233942},

issn = {2079-9292},

year  = {2022},

date = {2022-11-01},

urldate = {2022-01-01},

journal = {Electronics},

volume = {11},

number = {23},

abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.},

keywords = {Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics},

pubstate = {published},

tppubtype = {article}

}

Close
Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.
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https://www.mdpi.com/2079-9292/11/23/3942
doi:10.3390/electronics11233942
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2019
1.
 Burkhardt, Dirk;  Nazemi, Kawa;  Kuijper, Arjan;  Ginters, Egils
A Mobile Visual Analytics Approach for Instant Trend Analysis in Mobile Contexts Proceedings Article 
In: 5th International Conference of the Virtual and Augmented Reality in Education (VARE2019), pp. 11–19, CAL-TEK SRL, Rende, Italy, 2019, ISBN: 978-88-85741-41-6, (Nominated for Best Paper Award).
Abstract | Links | BibTeX | Tags: Business Analytics, Decision Support Systems, Human-Computer Interaction, Information visualization, Mobile Devices, Mobile Visual Analytics, Visual Trend Analysis
@inproceedings{Burkhardt2019bb,

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},

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}

}

Close
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.
Close
doi:10.5281/zenodo.3473041
Close