Publications
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. Kaupp, Lukas; Humm, Bernhard; Nazemi, Kawa; Simons, Stephan Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis Journal Article In: Sensors, vol. 22, no. 21, 2022, ISSN: 1424-8220. Nazemi, Kawa; Feiter, Tim; Sina, Lennart B.; Burkhardt, Dirk; Kock, Alexander Visual Analytics for Strategic Decision Making in Technology Management Book Chapter In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 31–61, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Ed.) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery Book SPRINGER NATURE, 2022, ISBN: 3030931188. Kovalerchuk, Boris; Andonie, Răzvan; Datia, Nuno; Nazemi, Kawa; Banissi, Ebad Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions Book Chapter In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 1–27, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Book Chapter In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Nazemi, Kawa; Burkhardt, Dirk; Kock, Alexander Visual analytics for Technology and Innovation Management: An interaction approach for strategic decisionmaking Journal Article In: Multimedia Tools and Applications, vol. 1198, 2021, ISSN: 1573-7721, (Springer Nature). Blazevic, Midhad; Sina, Lennart B.; Burkhardt, Dirk; Siegel, Melanie; Nazemi, Kawa Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Inproceedings In: 2021 25th International Conference Information Visualisation (IV), pp. 211-217, IEEE , 2021. Schütz, Mina; Schindler, Alexander; Siegel, Melanie; Nazemi, Kawa Automatic Fake News Detection with Pre-trained Transformer Models Inproceedings In: Bimbo, Alberto Del; Cucchiara, Rita; Sclaroff, Stan; Farinella, Giovanni Maria; Mei, Tao; Bertini, Marco; Escalante, Hugo Jair; Vezzani, Roberto (Ed.): Pattern Recognition. ICPR International Workshops and Challenges, pp. 627–641, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-68787-8. Sina, Lennart; Burkhardt, Dirk; Nazemi, Kawa Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts Inproceedings In: Afli, Haithem; Bleimann, Udo; Burkhardt, Dirk; Loew, Robert; Regier, Stefanie; Stengel, Ingo; Wang, Haiying; Zheng, Huiru (Jane) (Ed.): Proceedings of the 6th Collaborative European Research Conference (CERC 2020), pp. 222-235, CEUR-WS.org, Aachen, Germany, 2021, ISSN: 1613-0073, (urn:nbn:de:0074-2815-0). Nazemi, Kawa; Kaupp, Lukas; Burkhardt, Dirk; Below, Nicola Datenvisualisierung Book Chapter In: Heike Neuroth Markus Putnings, Jana Neumann (Ed.): Praxishandbuch Forschungsdatenmanagement, Chapter 5.4, pp. 477–502, De Gruyter, 2021, ISBN: 978-3-11-065365-6. Kaupp, Lukas; Webert, Heiko; Nazemi, Kawa; Humm, Bernhard; Simons, Stephan CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory Journal Article In: Procedia Computer Science, vol. 180, pp. 492-501, 2021, ISSN: 1877-0509, (Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)). Nazemi, Kawa; Kowald, Matthias; Dannewald, Till; Burkhardt, Dirk; Ginters, Egils Visual Analytics Indicators for Mobility and Transportation Inproceedings In: Grabis, Janis; Romanovs, Andrejs; Kulesova, Galina (Ed.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), pp. 1-6, IEEE, 2020, ISBN: 978-1-7281-9105-8. Aizstrauts, Artis; Burkhardt, Dirk; Ginters, Egils; Nazemi, Kawa On Microservice Architecture Based Communication Environment for Cycling Map Developing and Maintenance Simulator Inproceedings In: Grabis, Janis; Romanovs, Andrejs; Kulesova, Galina (Ed.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), pp. 1-4, IEEE, 2020, ISBN: 978-1-7281-9105-8. Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics Inproceedings In: Grabis, Janis; Romanovs, Andrejs; Kulesova, Galina (Ed.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), pp. 1-6, IEEE, 2020, ISBN: 978-1-7281-9105-8. Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing Inproceedings In: 2020 24th International Conference Information Visualisation (IV), pp. 350-359, IEEE Computer Society, 2020, ISSN: 2375-0138. Nazemi, Kawa; Klepsch, Maike J.; Burkhardt, Dirk; Kaupp, Lukas Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing Inproceedings In: 2020 24th International Conference Information Visualisation (IV), pp. 360-367, IEEE Computer Society, 2020, ISSN: 2375-0138. Banissi, Ebad; Khosrow-shahi, Farzad; Ursyn, Anna; Bannatyne, Mark W. McK.; Pires, João Moura; Datia, Nuno; Nazemi, Kawa; Kovalerchuk, Boris; Counsell, John; Agapiou, Andrew; Vrcelj, Zora; Chau, Hing-Wah; Li, Mengbi; Nagy, Gehan; Laing, Richard; Francese, Rita; Sarfraz, Muhammad; Bouali, Fatma; Venturin, Gilles; Trutschl, Marjan; Cvek, Urska; Müller, Heimo; Nakayama, Minoru; Temperini, Marco; Mascio, Tania Di; Rossano, Filippo SciarroneVeronica; Dörner, Ralf; Caruccio, Loredana; Vitiello, Autilia; Huang, Weidong; Risi, Michele; Erra, Ugo; Andonie, Razvan; Ahmad, Muhammad Aurangzeb; Figueiras, Ana; Mabakane, Mabule Samuel (Ed.) Proceedings of 2020 24th International Conference Information Visualisation (IV) Proceeding IEEE, New York, USA, 2020, ISBN: 978-1-7281-9134-8. Nazemi, Kawa; Burkhardt, Dirk; Kaupp, Lukas; Dannewald, Till; Kowald, Matthias; Ginters, Egils Visual Analytics in Mobility, Transportation and Logistics Inproceedings In: Ginters, Egils; Estrada, Mario Arturo Ruiz; Eroles, Miquel Angel Piera (Ed.): ICTE in Transportation and Logistics 2019, pp. 82–89, Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure, Cham, 2020, ISBN: 978-3-030-39688-6. Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations Inproceedings In: Ginters, Egils; Estrada, Mario Arturo Ruiz; Eroles, Miquel Angel Piera (Ed.): ICTE in Transportation and Logistics 2019, pp. 319–327, Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure, Cham, 2020, ISBN: 978-3-030-39688-6.2022
@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 = {},
pubstate = {published},
tppubtype = {article}
}
@article{s22218259,
title = {Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis},
author = {Lukas Kaupp and Bernhard Humm and Kawa Nazemi and Stephan Simons},
url = {https://www.mdpi.com/1424-8220/22/21/8259},
doi = {10.3390/s22218259},
issn = {1424-8220},
year = {2022},
date = {2022-10-01},
urldate = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {21},
abstract = {Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inbook{Nazemi2022,
title = {Visual Analytics for Strategic Decision Making in Technology Management},
author = {Kawa Nazemi and Tim Feiter and Lennart B. Sina and Dirk Burkhardt and Alexander Kock},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-030-93119-3_2},
doi = {10.1007/978-3-030-93119-3_2},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {31--61},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Strategic foresight, corporate foresight, and technology management enable firms to detect discontinuous changes early and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning methods and interactive visualizations. It enables a far better way to gather insights from a vast amount of data to make a strategic decision. While Visual Analytics got various models and approaches to enable strategic decision-making, the analysis of trends is still a matter of research. The forecasting approaches and involvement of humans in the visual trend analysis process require further investigation that will lead to sophisticated analytical methods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based on our previous work.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@book{2022,
title = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://link.springer.com/book/9783030931186, Springer Link},
isbn = {3030931188},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {SPRINGER NATURE},
series = {Studies in Computational Intelligence},
abstract = {This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.
This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.
The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.},
key = {SP2022},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.
The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.@inbook{Kovalerchuk2022b,
title = {Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions},
author = {Boris Kovalerchuk and Răzvan Andonie and Nuno Datia and Kawa Nazemi and Ebad Banissi},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-030-93119-3_1},
doi = {10.1007/978-3-030-93119-3_1},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {1--27},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Kaupp2022,
title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-030-93119-3_16},
doi = {10.1007/978-3-030-93119-3_16},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {403--436},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
2021
@article{Nazemi2021,
title = {Visual analytics for Technology and Innovation Management: An interaction approach for strategic decisionmaking},
author = {Kawa Nazemi and Dirk Burkhardt and Alexander Kock},
url = {https://link.springer.com/content/pdf/10.1007/s11042-021-10972-3.pdf, Open Access PDF},
doi = {10.1007/s11042-021-10972-3},
issn = {1573-7721},
year = {2021},
date = {2021-05-20},
urldate = {2021-05-20},
journal = {Multimedia Tools and Applications},
volume = {1198},
abstract = {The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.},
note = {Springer Nature},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9582711,
title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data},
author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi},
url = {https://ieeexplore.ieee.org/document/9582711, IEEE Xplore},
doi = {10.1109/IV53921.2021.00041},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
booktitle = {2021 25th International Conference Information Visualisation (IV)},
pages = {211-217},
publisher = {IEEE },
abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-030-68787-8_45,
title = {Automatic Fake News Detection with Pre-trained Transformer Models},
author = {Mina Schütz and Alexander Schindler and Melanie Siegel and Kawa Nazemi},
editor = {Alberto Del Bimbo and Rita Cucchiara and Stan Sclaroff and Giovanni Maria Farinella and Tao Mei and Marco Bertini and Hugo Jair Escalante and Roberto Vezzani},
url = {https://link.springer.com/chapter/10.1007/978-3-030-68787-8_45, Full PDF},
doi = {10.1007/978-3-030-68787-8_45},
isbn = {978-3-030-68787-8},
year = {2021},
date = {2021-02-21},
booktitle = {Pattern Recognition. ICPR International Workshops and Challenges},
pages = {627--641},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The automatic detection of disinformation and misinformation has gained attention during the last years, since fake news has a critical impact on democracy, society, and journalism and digital literacy. In this paper, we present a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture. The experiments were conducted on the FakeNewsNet dataset with XLNet, BERT, RoBERTa, DistilBERT, and ALBERT and various combinations of hyperparameters. Different preprocessing steps were carried out with only using the body text, the titles and a concatenation of both. It is concluded that Transformers are a promising approach to detect fake news, since they achieve notable results, even without using a large dataset. Our main contribution is the enhancement of fake news' detection accuracy through different models and parametrizations with a reproducible result examination through the conducted experiments. The evaluation shows that already short texts are enough to attain 85% accuracy on the test set. Using the body text and a concatenation of both reach up to 87% accuracy. Lastly, we show that various preprocessing steps, such as removing outliers, do not have a significant impact on the models prediction output.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Sina2021,
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 = {http://ceur-ws.org/Vol-2815/CERC2020_paper14.pdf, Paper on CEUR-WS, Full PDF},
issn = {1613-0073},
year = {2021},
date = {2021-02-17},
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{Nazemi2021a,
title = {Datenvisualisierung},
author = {Kawa Nazemi and Lukas Kaupp and Dirk Burkhardt and Nicola Below},
editor = {Markus Putnings, Heike Neuroth, Jana Neumann },
url = {https://doi.org/10.1515/9783110657807-026, Fulltext (open access)},
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},
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 = {},
pubstate = {published},
tppubtype = {inbook}
}
@article{KAUPP2021492,
title = {CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory},
author = {Lukas Kaupp and Heiko Webert and Kawa Nazemi and Bernhard Humm and Stephan Simons},
url = {https://www.sciencedirect.com/science/article/pii/S1877050921003148},
doi = {https://doi.org/10.1016/j.procs.2021.01.265},
issn = {1877-0509},
year = {2021},
date = {2021-01-01},
journal = {Procedia Computer Science},
volume = {180},
pages = {492-501},
abstract = {Cyber-physical systems in smart factories get more and more integrated and interconnected. Industry 4.0 accelerates this trend even further. Through the broad interconnectivity a new class of faults arise, the contextual faults, where contextual knowledge is needed to find the underlying reason. Fully-automated systems and the production line in a smart factory form a complex environment making the fault diagnosis non-trivial. Along with a dataset, we give a first definition of contextual faults in the smart factory and name initial use cases. Additionally, the dataset encompasses all the data recorded in a current state-of-the-art smart factory. We also add additional information measured by our developed sensing units to enrich the smart factory data even further. In the end, we show a first approach to detect the contextual faults in a manual preliminary analysis of the recorded log data.},
note = {Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
@inproceedings{Nazemi2020c,
title = {Visual Analytics Indicators for Mobility and Transportation},
author = {Kawa Nazemi and Matthias Kowald and Till Dannewald and Dirk Burkhardt and Egils Ginters},
editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},
doi = {10.1109/ITMS51158.2020.9259321},
isbn = {978-1-7281-9105-8},
year = {2020},
date = {2020-09-10},
booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},
pages = {1-6},
publisher = {IEEE},
abstract = {Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Aizstrauts2020c,
title = {On Microservice Architecture Based Communication Environment for Cycling Map Developing and Maintenance Simulator},
author = {Artis Aizstrauts and Dirk Burkhardt and Egils Ginters and Kawa Nazemi},
editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},
doi = {10.1109/ITMS51158.2020.9259299},
isbn = {978-1-7281-9105-8},
year = {2020},
date = {2020-09-09},
booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},
pages = {1-4},
publisher = {IEEE},
abstract = {Urban transport infrastructure nowadays involves environmentally friendly modes of transport, the most democratic of which is cycling. Citizens will use bicycles if a reasonably designed cycle path scheme will be provided. Cyclists also need to know the characteristics and load of the planned route before the trip. Prediction can be provided by simulation, but it is often necessary to use heterogeneous and distributed models that require a specific communication environment to ensure interaction. The article describes the easy communication environment that is used to provide microservices communication and data exchange in a bicycle route design and maintenance multi-level simulator.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Burkhardt2020cb,
title = {Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics},
author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},
editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},
doi = {10.1109/ITMS51158.2020.9259309},
isbn = {978-1-7281-9105-8},
year = {2020},
date = {2020-09-09},
booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},
pages = {1-6},
publisher = {IEEE},
abstract = {A variety of new technologies and ideas for businesses are arising in the domain of logistics and mobility. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users’ behavior is commonly investigated in adaptive systems, which is considering the induvial preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows generating a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Kaupp_IV2020,
title = {An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
doi = {10.1109/IV51561.2020.00064},
issn = {2375-0138},
year = {2020},
date = {2020-09-01},
booktitle = {2020 24th International Conference Information Visualisation (IV)},
pages = {350-359},
publisher = {IEEE Computer Society},
abstract = {The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi_IV2020,
title = {Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing},
author = {Kawa Nazemi and Maike J. Klepsch and Dirk Burkhardt and Lukas Kaupp},
doi = {10.1109/IV51561.2020.00065},
issn = {2375-0138},
year = {2020},
date = {2020-09-01},
booktitle = {2020 24th International Conference Information Visualisation (IV)},
pages = {360-367},
publisher = {IEEE Computer Society},
abstract = {Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@proceedings{Banissi2020,
title = {Proceedings of 2020 24th International Conference Information Visualisation (IV)},
editor = {Ebad Banissi and Farzad Khosrow-shahi and Anna Ursyn and Mark W. McK. Bannatyne and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and John Counsell and Andrew Agapiou and Zora Vrcelj and Hing-Wah Chau and Mengbi Li and Gehan Nagy and Richard Laing and Rita Francese and Muhammad Sarfraz and Fatma Bouali and Gilles Venturin and Marjan Trutschl and Urska Cvek and Heimo Müller and Minoru Nakayama and Marco Temperini and Tania Di Mascio and Filippo SciarroneVeronica Rossano and Ralf Dörner and Loredana Caruccio and Autilia Vitiello and Weidong Huang and Michele Risi and Ugo Erra and Razvan Andonie and Muhammad Aurangzeb Ahmad and Ana Figueiras and Mabule Samuel Mabakane},
doi = {10.1109/IV51561.2020},
isbn = {978-1-7281-9134-8},
year = {2020},
date = {2020-09-01},
urldate = {2020-09-01},
booktitle = {Information Visualisation: AI & Analytics, Biomedical Visualization, Builtviz, and Geometric Modelling & Imaging},
pages = {1-775},
publisher = {IEEE},
address = {New York, USA},
abstract = {In the current information era, most aspects of life depend on and are driven by data, information, knowledge, user experience, and cultural influences. The infrastructure of any information-dependent society relies on the quality of data, information and analysis of such entities for short to long term as well as past and future activities. Information Visualisation, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science, an aspect that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition such that its dependencies on the domain of application and its evolution steer the next generation of research activities. Processing the relationship between these phases, from the raw data to knowledge, has added new impetus to the way these are understood and communicated. The tradition of use and communication by visualisation is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience and leads to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualisation have added momentum in developing tools that exploit 2D and 3D metaphor-driven techniques within many applied domains. The techniques are developed beyond visualisation to simplify the complexities, to reveal ambiguity, and to work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; how this uncertainty is built into the processes that exist in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualisation forum, compiled for the 24th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine- & deeplearning - Biomedical Visualization, Learning Analytics & Geometric Modelling and Imaging - IV2020, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2020 provides the opportunity to resonate with many international and collaborative research projects as well as lectures from distinguished speakers that channels the way this new framework conceptually, as well as practically has been realised. This year's theme is enhanced further by AI, Social Networks impact on social, cultural and heritage aspect of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 100 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualisation, analytics, applications, and results of the work of researchers, artists and professionals from more than 25 countries. It has allowed us to address the scope of visualisation from a much broader perspective. Each contributor to this conference has indeed added fresh perspectives and thoughts, challenges our beliefs and encouraged further our adventure of innovation.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
This collection of papers on this year's information visualisation forum, compiled for the 24th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine- & deeplearning - Biomedical Visualization, Learning Analytics & Geometric Modelling and Imaging - IV2020, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2020 provides the opportunity to resonate with many international and collaborative research projects as well as lectures from distinguished speakers that channels the way this new framework conceptually, as well as practically has been realised. This year's theme is enhanced further by AI, Social Networks impact on social, cultural and heritage aspect of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 100 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualisation, analytics, applications, and results of the work of researchers, artists and professionals from more than 25 countries. It has allowed us to address the scope of visualisation from a much broader perspective. Each contributor to this conference has indeed added fresh perspectives and thoughts, challenges our beliefs and encouraged further our adventure of innovation.
@inproceedings{10.1007/978-3-030-39688-6_12,
title = {Visual Analytics in Mobility, Transportation and Logistics},
author = {Kawa Nazemi and Dirk Burkhardt and Lukas Kaupp and Till Dannewald and Matthias Kowald and Egils Ginters},
editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},
url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_12, Springer},
doi = {10.1007/978-3-030-39688-6_12},
isbn = {978-3-030-39688-6},
year = {2020},
date = {2020-01-31},
booktitle = {ICTE in Transportation and Logistics 2019},
pages = {82--89},
publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},
address = {Cham},
abstract = {Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans' mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. New technologies will lead to a different mobility behavior with new constraints. These changes in mobility behavior and logistics require analytical systems to forecast the required information and probably appearing changes. These systems have to consider different perspectives and employ multiple indicators. Visual Analytics provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary case studies to illustrate the advantages of such systems. The examples are aimed to demonstrate the benefits of Visual Analytics in mobility.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-030-39688-6_40,
title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations},
author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},
editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},
url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_40, Springer },
doi = {10.1007/978-3-030-39688-6_40},
isbn = {978-3-030-39688-6},
year = {2020},
date = {2020-01-30},
booktitle = {ICTE in Transportation and Logistics 2019},
pages = {319--327},
publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},
address = {Cham},
abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
orcid.org/0000-0002-2907-2740