Publikationen ⋆ Kawa Nazemi

Publikationen


Zeige alle
2022
3.
 Kaupp, Lukas;  Nazemi, Kawa;  Humm, Bernhard
Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories Artikel 
In: Electronics, Bd. 11, Nr. 23, 2022, ISSN: 2079-9292.
Abstract | Links | BibTeX | Schlagwörter: 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}

}

Schließen
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.
Schließen
https://www.mdpi.com/2079-9292/11/23/3942
doi:10.3390/electronics11233942
Schließen
2012
2.
 Stab, Christian;  Breyer, Matthias;  Burkhardt, Dirk;  Nazemi, Kawa;  Kohlhammer, Jörn
Analytical semantics visualization for discovering latent signals in large text collections Proceedings Article 
In:  Kerren, Andreas;  Seipel, Stefan (Hrsg.): Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden, S. 83–86, Linköping University Linköping University Electronic Press, 2012, ISBN: 978-91-7519-723-4.
Abstract | Links | BibTeX | Schlagwörter: Data Analytics, Data visualization, Semantic data modeling, Visual analytics
@inproceedings{stab2012analytical,

title = {Analytical semantics visualization for discovering latent signals in large text collections},

author = {Christian Stab and Matthias Breyer and Dirk Burkhardt and Kawa Nazemi and Jörn Kohlhammer},

editor = {Andreas Kerren and Stefan Seipel},

url = {https://www.ep.liu.se/ecp/081/011/ecp12081011.pdf, full text},

isbn = {978-91-7519-723-4},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden},

number = {081},

pages = {83--86},

publisher = {Linköping University Electronic Press},

organization = {Linköping University},

abstract = {Considering the increasing pressure of competition and high dynamics of markets; the early identification and specific handling of novel developments and trends becomes more and more important for competitive companies. Today; those signals are encoded in large amounts of textual data like competitors’ web sites; news articles; scientific publications or blog entries which are freely available in the web. Processing large amounts of textual data is still a tremendous challenge for current business analysts and strategic decision makers. Although current information systems are able to process that amount of data and provide a wide range of information retrieval tools; it is almost impossible to keep track of each thread or opportunity. The presented approach combines semantic search and data mining techniques with interactive visualizations for analyzing and identifying weak signals in large text collections. Beside visual summarization tools; it includes an enhanced trend visualization that supports analysts in identifying latent topic-related relations between competitors and their temporal relevance. It includes a graph-based visualization tool for representing relations identified during semantic analysis. The interaction design allows analysts to verify their retrieved hypothesis by exploring the documents that are responsible for the current view.},

keywords = {Data Analytics, Data visualization, Semantic data modeling, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Considering the increasing pressure of competition and high dynamics of markets; the early identification and specific handling of novel developments and trends becomes more and more important for competitive companies. Today; those signals are encoded in large amounts of textual data like competitors’ web sites; news articles; scientific publications or blog entries which are freely available in the web. Processing large amounts of textual data is still a tremendous challenge for current business analysts and strategic decision makers. Although current information systems are able to process that amount of data and provide a wide range of information retrieval tools; it is almost impossible to keep track of each thread or opportunity. The presented approach combines semantic search and data mining techniques with interactive visualizations for analyzing and identifying weak signals in large text collections. Beside visual summarization tools; it includes an enhanced trend visualization that supports analysts in identifying latent topic-related relations between competitors and their temporal relevance. It includes a graph-based visualization tool for representing relations identified during semantic analysis. The interaction design allows analysts to verify their retrieved hypothesis by exploring the documents that are responsible for the current view.
Schließen
full text
Schließen
2011
1.
 Nazemi, Kawa;  Breyer, Matthias;  Kuijper, Arjan
User-Oriented Graph Visualization Taxonomy: A Data-Oriented Examination of Visual Features Konferenz 
Human Centered Design, LNCS 6776 Springer Berlin Heidelberg, 2011, ISBN: 978-3-642-21753-1.
Abstract | Links | BibTeX | Schlagwörter: Data visualization, Graph visualization, Taxonomies
@conference{C35-P-22203,

title = {User-Oriented Graph Visualization Taxonomy: A Data-Oriented Examination of Visual Features},

author = {Kawa Nazemi and Matthias Breyer and Arjan Kuijper},

editor = {Masaaki Kurosu},

url = {https://doi.org/10.1007/978-3-642-21753-1_64, DOI

https://link.springer.com/chapter/10.1007/978-3-642-21753-1_64, Springer page},

doi = {10.1007/978-3-642-21753-1_64},

isbn = {978-3-642-21753-1},

year  = {2011},

date = {2011-01-01},

booktitle = {Human Centered Design},

pages = {576-585},

publisher = {Springer Berlin Heidelberg},

series = {LNCS 6776},

abstract = {Presenting information in a user-oriented way has a significant impact on the success and comprehensibility of data visualizations. In order to correctly and comprehensibly visualize data in a user-oriented way data specific aspects have to be considered. Furthermore, user-oriented perception characteristics are decisive for the fast and proper interpretation of the visualized data. In this paper we present a taxonomy for graph visualization techniques. On the one hand it provides the user-oriented identification of applicable visual features for given data to be visualized. On the other hand the set of visualization techniques is enclosed which supports these identified visual features. Thus, the taxonomy supports the development of user-oriented visualizations by examination of data to obtain a beneficial association of data to visual features.},

keywords = {Data visualization, Graph visualization, Taxonomies},

pubstate = {published},

tppubtype = {conference}

}

Schließen
Presenting information in a user-oriented way has a significant impact on the success and comprehensibility of data visualizations. In order to correctly and comprehensibly visualize data in a user-oriented way data specific aspects have to be considered. Furthermore, user-oriented perception characteristics are decisive for the fast and proper interpretation of the visualized data. In this paper we present a taxonomy for graph visualization techniques. On the one hand it provides the user-oriented identification of applicable visual features for given data to be visualized. On the other hand the set of visualization techniques is enclosed which supports these identified visual features. Thus, the taxonomy supports the development of user-oriented visualizations by examination of data to obtain a beneficial association of data to visual features.
Schließen
DOI
Springer page
doi:10.1007/978-3-642-21753-1_64
Schließen