2022 |
| 5. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm 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. @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},
doi = {10.3390/electronics11233942},
issn = {2079-9292},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
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}
}
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. |
2021 |
| 4. | Kawa Nazemi; Lukas Kaupp; Dirk Burkhardt; Nicola Below Datenvisualisierung Buchkapitel In: Markus Putnings; Heike Neuroth; Janna Neumann (Hrsg.): Praxishandbuch Forschungsdatenmanagement, Kapitel 5.4, S. 477-502, De Gruyter, Berlin/Boston, 2021, ISBN: 978-3-11-065365-6. @inbook{Nazemi2021,
title = {Datenvisualisierung},
author = {Kawa Nazemi and Lukas Kaupp and Dirk Burkhardt and Nicola Below},
editor = {Markus Putnings and Heike Neuroth and Janna Neumann},
doi = {10.1515/9783110657807-026},
isbn = {978-3-11-065365-6},
year = {2021},
date = {2021-01-18},
booktitle = {Praxishandbuch Forschungsdatenmanagement},
pages = {477-502},
publisher = {De Gruyter},
address = {Berlin/Boston},
chapter = {5.4},
abstract = {Die visuelle Projektion von heterogenen (z. B. Forschungs-)Daten auf einer 2-dimensionalen Fläche, wie etwa einem Bildschirm, wird als Datenvisualisierung bezeichnet. Datenvisualisierung ist ein Oberbegriff für verschiedene Arten der visuellen Projektion. In diesem Kapitel wird zunächst der Begriff definiert und abgegrenzt. Der Fokus des Kapitels liegt auf Informationsvisualisierung und Visual Analytics. In diesem Kontext wird der Prozess der visuellen Transformation vorgestellt. Es soll als Grundlage für eine wissenschaftlich valide Generierung von Visualisierungen dienen, die auch visuelle Aufgaben umfassen. Anwendungsszenarien stellen den Mehrwert der hier vorgestellten Konzepte in der Praxis vor. Der wissenschaftliche Beitrag liegt in einer formalen Definition des visuellen Mappings.},
keywords = {Data Visualization},
pubstate = {published},
tppubtype = {inbook}
}
Die visuelle Projektion von heterogenen (z. B. Forschungs-)Daten auf einer 2-dimensionalen Fläche, wie etwa einem Bildschirm, wird als Datenvisualisierung bezeichnet. Datenvisualisierung ist ein Oberbegriff für verschiedene Arten der visuellen Projektion. In diesem Kapitel wird zunächst der Begriff definiert und abgegrenzt. Der Fokus des Kapitels liegt auf Informationsvisualisierung und Visual Analytics. In diesem Kontext wird der Prozess der visuellen Transformation vorgestellt. Es soll als Grundlage für eine wissenschaftlich valide Generierung von Visualisierungen dienen, die auch visuelle Aufgaben umfassen. Anwendungsszenarien stellen den Mehrwert der hier vorgestellten Konzepte in der Praxis vor. Der wissenschaftliche Beitrag liegt in einer formalen Definition des visuellen Mappings. |
2019 |
| 3. | Kawa Nazemi; Dirk Burkhardt Visual Analytics for Analyzing Technological Trends from Text Konferenzbeitrag In: 2019 23rd International Conference Information Visualisation (IV), S. 191-200, IEEE, 2019, ISSN: 2375-0138, (Best Paper Award). @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}
}
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. |
2012 |
| 2. | Christian Stab; Matthias Breyer; Dirk Burkhardt; Kawa Nazemi; Jörn Kohlhammer Analytical semantics visualization for discovering latent signals in large text collections Konferenzbeitrag In: Andreas Kerren; Stefan Seipel (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. @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 = {http://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}
}
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. |
2011 |
| 1. | Kawa Nazemi; Matthias Breyer; Arjan Kuijper 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. @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}
}
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. |
