Publications ⋆ Kawa Nazemi

Publications


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2023
14.
 Nazemi, Kawa
Artificial Intelligence in Visual Analytics Proceedings Article 
In: Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award, pp. 230 - 237, IEEE CPS, 2023.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual tasks
@inproceedings{Nazemi2023,

title = {Artificial Intelligence in Visual Analytics},

author = {Kawa Nazemi},

doi = {10.1109/IV60283.2023.00048},

year  = {2023},

date = {2023-11-30},

urldate = {2023-11-30},

booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award},

journal = {Proceedings of the 27th International Conference Information Visualisation (IV2023) - Best Paper Award-},

pages = {230 - 237},

publisher = {IEEE CPS},

abstract = {Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.},

keywords = {Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual tasks},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.
Close
doi:10.1109/IV60283.2023.00048
Close
13.
 Sina, Lennart B.;  Secco, Cristian A.;  Blazevic, Midhad;  Nazemi, Kawa
Visual Analytics for Corporate Foresight - A Conceptual Approach Proceedings Article 
In: Proceedings of the 27th International Conference Information Visualisation (IV2023), pp. 244-250, IEEE CPS, 2023.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Visual analytics
@inproceedings{SinaIV2023,

title = {Visual Analytics for Corporate Foresight - A Conceptual Approach},

author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},

doi = {10.1109/IV60283.2023.00050},

year  = {2023},

date = {2023-11-29},

urldate = {2023-11-29},

booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},

pages = {244-250},

publisher = {IEEE CPS},

abstract = {Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data.},

keywords = {Artificial Intelligence, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data.
Close
doi:10.1109/IV60283.2023.00050
Close
12.
 Banissi, Ebad;  Siirtola, Harri;  Ursyn, Anna;  Pires, João Moura;  Datia, Nuno;  Nazemi, Kawa;  Kovalerchuk, Boris;  Andonie, Razvan;  Nakayama, Minoru;  Temperini, Marco;  Sciarrone, Filippo;  Nguyen, Quang Vinh;  Mabakane, Mabule Samuel;  Rusu, Adrian;  Cvek, Urska;  Trutschl, Marjan;  Mueller, Heimo;  Francese, Rita;  Boua-li, Fatma;  Venturini, Gilles (Ed.)
Proceedings of 2023 27th International Conference Information Visualisation Proceedings 
2023, ISBN: 979-8-3503-4161-4.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery
@proceedings{Banissi2023,

title = {Proceedings of 2023 27th International Conference Information Visualisation},

editor = {Ebad Banissi and Harri Siirtola and Anna Ursyn and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and Razvan Andonie and Minoru Nakayama and Marco Temperini and Filippo Sciarrone and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Urska Cvek and Marjan Trutschl and Heimo Mueller and Rita Francese and Fatma Boua-li and Gilles Venturini},

doi = {10.1109/IV60283.2023.00001},

isbn = {979-8-3503-4161-4},

year  = {2023},

date = {2023-11-01},

urldate = {2023-11-01},

issue = {IV2023},

abstract = {Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt.},

keywords = {Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery},

pubstate = {published},

tppubtype = {proceedings}

}

Close
Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt.
Close
doi:10.1109/IV60283.2023.00001
Close
2022
11.
 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.
Close
https://www.mdpi.com/2079-9292/11/23/3942
doi:10.3390/electronics11233942
Close
10.
 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.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Machine Leanring, Machine learning, smart factory, Smart manufacturing
@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 = {Artificial Intelligence, Machine Leanring, Machine learning, smart factory, Smart manufacturing},

pubstate = {published},

tppubtype = {article}

}

Close
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&mdash;always on&mdash;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.
Close
https://www.mdpi.com/1424-8220/22/21/8259
doi:10.3390/s22218259
Close
9.
 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.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery
@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 = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},

pubstate = {published},

tppubtype = {inbook}

}

Close
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.
Close
https://doi.org/10.1007/978-3-030-93119-3_16
doi:10.1007/978-3-030-93119-3_16
Close
8.
 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.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery
@inbook{Kaupp2022b,

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},

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 = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},

pubstate = {published},

tppubtype = {inbook}

}

Close
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.
Close
doi:10.1007/978-3-030-93119-3_16
Close
2021
7.
 Blazevic, Midhad;  Sina, Lennart B.;  Burkhardt, Dirk;  Siegel, Melanie;  Nazemi, Kawa
Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Proceedings Article 
In: 2021 25th International Conference Information Visualisation (IV), pp. 211-217, IEEE , 2021.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics
@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 = {Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
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.
Close
IEEE Xplore
doi:10.1109/IV53921.2021.00041
Close
6.
 Schütz, Mina;  Schindler, Alexander;  Siegel, Melanie;  Nazemi, Kawa
Automatic Fake News Detection with Pre-trained Transformer Models Proceedings Article 
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.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, datamining, Decision Making, Fake News, Machine Leanring, Transformer
@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 = {Artificial Intelligence, datamining, Decision Making, Fake News, Machine Leanring, Transformer},

pubstate = {published},

tppubtype = {inproceedings}

}

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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.
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doi:10.1007/978-3-030-68787-8_45
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2020
5.
 Nazemi, Kawa;  Kowald, Matthias;  Dannewald, Till;  Burkhardt, Dirk;  Ginters, Egils
Visual Analytics Indicators for Mobility and Transportation Proceedings Article 
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.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Mobility, Visual analytics
@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 = {Artificial Intelligence, Mobility, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
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.
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doi:10.1109/ITMS51158.2020.9259321
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4.
 Nazemi, Kawa;  Klepsch, Maike J.;  Burkhardt, Dirk;  Kaupp, Lukas
Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing Proceedings Article 
In: 2020 24th International Conference Information Visualisation (IV), pp. 360-367, IEEE Computer Society, 2020, ISSN: 2375-0138.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Data Science, Information Science, Information visualization, Large scale integration, Libraries, Machine Leanring, Market Research, Natural Language Processing, Visual analytics, Visual Trend Analytics
@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 = {Artificial Intelligence, Data Science, Information Science, Information visualization, Large scale integration, Libraries, Machine Leanring, Market Research, Natural Language Processing, Visual analytics, Visual Trend Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
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.
Close
doi:10.1109/IV51561.2020.00065
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3.
 Nazemi, Kawa;  Burkhardt, Dirk;  Kaupp, Lukas;  Dannewald, Till;  Kowald, Matthias;  Ginters, Egils
Visual Analytics in Mobility, Transportation and Logistics Proceedings Article 
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.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Mobility, Visual analytics
@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 = {Artificial Intelligence, Mobility, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
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.
Close
Springer
doi:10.1007/978-3-030-39688-6_12
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2019
2.
 Nazemi, Kawa;  Burkhardt, Dirk
A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management Proceedings Article 
In:  Bebis, George;  Boyle, Richard;  Parvin, Bahram;  Koracin, Darko;  Ushizima, Daniela;  Chai, Sek;  Sueda, Shinjiro;  Lin, Xin;  Lu, Aidong;  Thalmann, Daniel;  Wang, Chaoli;  Xu, Panpan (Ed.): Advances in Visual Computing, pp. 283–294, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-33723-0.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Machine Leanring, Visual analytics
@inproceedings{Nazemi_ISVC2019,

title = {A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management},

author = {Kawa Nazemi and Dirk Burkhardt},

editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu},

url = {https://rd.springer.com/chapter/10.1007/978-3-030-33723-0_23, Springer LNCS},

doi = {10.1007/978-3-030-33723-0_23},

isbn = {978-3-030-33723-0},

year  = {2019},

date = {2019-10-09},

booktitle = {Advances in Visual Computing},

pages = {283--294},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.},

keywords = {Artificial Intelligence, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Machine Leanring, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.
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Springer LNCS
doi:10.1007/978-3-030-33723-0_23
Close
1.
 Nazemi, K;  Burkhardt, D
Visual Analytics for Analyzing Technological Trends from Text Proceedings Article 
In: 2019 23rd International Conference Information Visualisation (IV), pp. 191-200, 2019, ISSN: 2375-0138, (Best Paper Award).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Information visualization, Machine Leanring, Market research;Visualization;Data mining;Data visualization;Data models;Hidden Markov models;Patents;Visual Analytics;information visualization;trend analytics;emerging trend identification;visual business analytics, Visual analytics
@inproceedings{Nazemi-IV2019,

title = {Visual Analytics for Analyzing Technological Trends from Text},

author = {K Nazemi and D Burkhardt},

doi = {10.1109/IV.2019.00041},

issn = {2375-0138},

year  = {2019},

date = {2019-07-01},

booktitle = {2019 23rd International Conference Information Visualisation (IV)},

pages = {191-200},

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, Information visualization, Machine Leanring, Market research;Visualization;Data mining;Data visualization;Data models;Hidden Markov models;Patents;Visual Analytics;information visualization;trend analytics;emerging trend identification;visual business analytics, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

Close
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.
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doi:10.1109/IV.2019.00041
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