2022 |
| 123. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, pp. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. @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},
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}
}
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. |
2021 |
| 122. | Ebad Banissi; Anna Ursyn; Mark W. McK. Bannatyne; João Moura Pires; Nuno Datia; Mao Lin Huang Weidong Huang; Quang Vinh Nguyen; Kawa Nazemi; Boris Kovalerchuk; Minoru Nakayama; John Counsell; Andrew Agapiou; Farzad Khosrow-shahi; Hing-Wah Chau; Mengbi Li; Richard Laing; Fatma Bouali; Gilles Venturini; Marco Temperini; Muhammad Sarfraz (Ed.) Proceedings of 2021 25th International Conference Information Visualisation (IV) Proceeding IEEE, New York, USA, 2021, ISBN: 978-1-6654-3827-8. @proceedings{Banissi2021,
title = {Proceedings of 2021 25th International Conference Information Visualisation (IV)},
editor = {Ebad Banissi and Anna Ursyn and Mark W. McK. Bannatyne and João Moura Pires and Nuno Datia and Mao Lin Huang Weidong Huang and Quang Vinh Nguyen and Kawa Nazemi and Boris Kovalerchuk and Minoru Nakayama and John Counsell and Andrew Agapiou and Farzad Khosrow-shahi and Hing-Wah Chau and Mengbi Li and Richard Laing and Fatma Bouali and Gilles Venturini and Marco Temperini and Muhammad Sarfraz},
doi = {10.1109/IV53921.2021.00001},
isbn = {978-1-6654-3827-8},
year = {2021},
date = {2021-10-28},
urldate = {2021-10-28},
booktitle = {Information Visualisation: AI & Analytics, Biomedical Visualization, Builtviz, and Geometric Modelling & Imaging},
pages = {1-775},
publisher = {IEEE},
address = {New York, USA},
abstract = {Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any informationdependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it intended to be applied. Information Visualization, 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. Aspects that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the domain of application and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. 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 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. 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 the processes in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, 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, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75 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 visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation.},
keywords = {Information visualization},
pubstate = {published},
tppubtype = {proceedings}
}
Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any informationdependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it intended to be applied. Information Visualization, 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. Aspects that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the domain of application and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. 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 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. 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 the processes in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, 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, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75 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 visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation. |
| 121. | Felix Bach; Stefan Schmunk; Cristian Secco; Thorsten Wübbena Bomber’s Baedeker – vom Text zum Bild zur Datenquelle Journal Article In: Fabrikation von Erkenntnis – Experimente in den Digital Humanities, 2021. @article{BSS*21,
title = {Bomber’s Baedeker – vom Text zum Bild zur Datenquelle},
author = {Felix Bach and Stefan Schmunk and Cristian Secco and Thorsten Wübbena},
editor = {Manuel Burghardt, Lisa Dieckmann, Timo Steyer, Peer Trilcke, Niels Walkowski, Joëlle Weis, Ulrike Wuttke},
doi = {10.17175/sb005_004},
year = {2021},
date = {2021-09-22},
urldate = {2021-09-22},
journal = {Fabrikation von Erkenntnis – Experimente in den Digital Humanities},
abstract = {The two-volume printed work The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities was produced by the British Foreign Office and the Ministry of Economic Warfare during the Second World War. It lists towns and cities of the German Reich with more than a thousand inhabitants and information on their war-related infrastructure, industrial and production facilities. Only four verified copies still exist worldwide and none of them has been digitally accessible for scholarly use until now. In 2019, The Bomber's Baedeker was (re-)discovered in the library of the Leibniz Institute of European History (IEG), digitised in cooperation with the University Library of Mainz and made accessible and processed in a cross-institutional cooperation between the Digital Historical Research Unit | DH Lab of the IEG and the Darmstadt University of Applied Sciences, including in courses with students, so that The Bomber's Baedeker can now be used, analysed and further processed as an open, machine-readable data source in compliance with the FAIR principles.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The two-volume printed work The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities was produced by the British Foreign Office and the Ministry of Economic Warfare during the Second World War. It lists towns and cities of the German Reich with more than a thousand inhabitants and information on their war-related infrastructure, industrial and production facilities. Only four verified copies still exist worldwide and none of them has been digitally accessible for scholarly use until now. In 2019, The Bomber's Baedeker was (re-)discovered in the library of the Leibniz Institute of European History (IEG), digitised in cooperation with the University Library of Mainz and made accessible and processed in a cross-institutional cooperation between the Digital Historical Research Unit | DH Lab of the IEG and the Darmstadt University of Applied Sciences, including in courses with students, so that The Bomber's Baedeker can now be used, analysed and further processed as an open, machine-readable data source in compliance with the FAIR principles. |
| 120. | Jaqueline Böck; Daria Liakhovets; Mina Schütz; Armin Kirchknopf; Djordje Slijepčevi'c; Matthias Zeppelzauer; Alexander Schindler AIT_FHSTP at GermEval 2021: Automatic Fact Claiming Detection with Multilingual Transformer Models Inproceedings In: Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, pp. 76–82, Association for Computational Linguistics, Duesseldorf, Germany, 2021. @inproceedings{BLSK21,
title = {AIT_FHSTP at GermEval 2021: Automatic Fact Claiming Detection with Multilingual Transformer Models},
author = {Jaqueline Böck and Daria Liakhovets and Mina Schütz and Armin Kirchknopf and Djordje Slijepčevi'c and Matthias Zeppelzauer and Alexander Schindler},
url = {https://aclanthology.org/2021.germeval-1.11},
year = {2021},
date = {2021-09-01},
booktitle = {Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments},
pages = {76--82},
publisher = {Association for Computational Linguistics},
address = {Duesseldorf, Germany},
abstract = {Spreading ones opinion on the internet is becoming more and more important. A problem is that in many discussions people often argue with supposed facts. This year's GermEval 2021 focuses on this topic by incorporating a shared task on the identification of fact-claiming comments. This paper presents the contribution of the AIT FHSTP team at the GermEval 2021 benchmark for task 3: ``identifying fact-claiming comments in social media texts''. Our methodological approaches are based on transformers and incorporate 3 different models: multilingual BERT, GottBERT and XML-RoBERTa. To solve the fact claiming task, we fine-tuned these transformers with external data and the data provided by the GermEval task organizers. Our multilingual BERT model achieved a precision-score of 72.71%, a recall of 72.96% and an F1-Score of 72.84% on the GermEval test set. Our fine-tuned XML-RoBERTa model achieved a precision-score of 68.45%, a recall of 70.11% and a F1-Score of 69.27%. Our best model is GottBERT (i.e., a BERT transformer pre-trained on German texts) fine-tuned on the GermEval 2021 data. This transformer achieved a precision of 74.13%, a recall of 75.11% and an F1-Score of 74.62% on the test set.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spreading ones opinion on the internet is becoming more and more important. A problem is that in many discussions people often argue with supposed facts. This year's GermEval 2021 focuses on this topic by incorporating a shared task on the identification of fact-claiming comments. This paper presents the contribution of the AIT FHSTP team at the GermEval 2021 benchmark for task 3: ``identifying fact-claiming comments in social media texts''. Our methodological approaches are based on transformers and incorporate 3 different models: multilingual BERT, GottBERT and XML-RoBERTa. To solve the fact claiming task, we fine-tuned these transformers with external data and the data provided by the GermEval task organizers. Our multilingual BERT model achieved a precision-score of 72.71%, a recall of 72.96% and an F1-Score of 72.84% on the GermEval test set. Our fine-tuned XML-RoBERTa model achieved a precision-score of 68.45%, a recall of 70.11% and a F1-Score of 69.27%. Our best model is GottBERT (i.e., a BERT transformer pre-trained on German texts) fine-tuned on the GermEval 2021 data. This transformer achieved a precision of 74.13%, a recall of 75.11% and an F1-Score of 74.62% on the test set. |
| 119. | Mina Schütz; Christoph Demus; Jonas Pitz; Nadine Probol; Melanie Siegel; Dirk Labudde DeTox at GermEval 2021: Toxic Comment Classification Inproceedings In: Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, pp. 54–61, Association for Computational Linguistics, Duesseldorf, Germany, 2021. @inproceedings{SDPP21,
title = {DeTox at GermEval 2021: Toxic Comment Classification},
author = {Mina Schütz and Christoph Demus and Jonas Pitz and Nadine Probol and Melanie Siegel and Dirk Labudde},
url = {https://aclanthology.org/2021.germeval-1.8},
year = {2021},
date = {2021-09-01},
booktitle = {Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments},
pages = {54--61},
publisher = {Association for Computational Linguistics},
address = {Duesseldorf, Germany},
abstract = {In this work, we present our approaches on the toxic comment classification task (subtask 1) of the GermEval 2021 Shared Task. For this binary task, we propose three models: a German BERT transformer model; a multilayer perceptron, which was first trained in parallel on textual input and 14 additional linguistic features and then concatenated in an additional layer; and a multilayer perceptron with both feature types as input. We enhanced our pre-trained transformer model by re-training it with over 1 million tweets and fine-tuned it on two additional German datasets of similar tasks. The embeddings of the final fine-tuned German BERT were taken as the textual input features for our neural networks. Our best models on the validation data were both neural networks, however our enhanced German BERT gained with a F1-score = 0.5895 a higher prediction on the test data.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work, we present our approaches on the toxic comment classification task (subtask 1) of the GermEval 2021 Shared Task. For this binary task, we propose three models: a German BERT transformer model; a multilayer perceptron, which was first trained in parallel on textual input and 14 additional linguistic features and then concatenated in an additional layer; and a multilayer perceptron with both feature types as input. We enhanced our pre-trained transformer model by re-training it with over 1 million tweets and fine-tuned it on two additional German datasets of similar tasks. The embeddings of the final fine-tuned German BERT were taken as the textual input features for our neural networks. Our best models on the validation data were both neural networks, however our enhanced German BERT gained with a F1-score = 0.5895 a higher prediction on the test data. |
| 118. | Kawa Nazemi; Dirk Burkhardt; Alexander Kock 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. @article{Nazemi2021b,
title = {Visual analytics for technology and innovation management: An interaction approach for strategic decisionmaking },
author = {Kawa Nazemi and Dirk Burkhardt and Alexander Kock},
editor = {Rita Francese and Borko Furht},
doi = {10.1007/s11042-021-10972-3},
issn = {1573-7721},
year = {2021},
date = {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.},
keywords = {emerging trend identification, Information visualization, Innovation Management, Interaction Design, Multimodal Interaction, Technology Management, Visual analytics, Visual Trend Analytics},
pubstate = {published},
tppubtype = {article}
}
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. |
| 117. | Mina Schütz; Alexander Schindler; Melanie Siegel; Kawa Nazemi Automatic Fake News Detection with Pre-trained Transformer Models Inproceedings In: Alberto Del Bimbo; Rita Cucchiara; Stan Sclaroff; Giovanni Maria Farinella; Tao Mei; Marco Bertini; Hugo Jair Escalante; Roberto Vezzani (Ed.): Pattern Recognition. ICPR International Workshops and Challenges, pp. 627–641, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-68787-8. @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},
doi = {10.1007/978-3-030-68787-8_45},
isbn = {978-3-030-68787-8},
year = {2021},
date = {2021-02-21},
urldate = {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, Data Analytics, Data Mining, Fake News, maschine learning, Transformer},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 116. | Haithem Afli; Udo Bleimann; Dirk Burkhardt; Robert Loew; Stefanie Regier; Ingo Stengel; Haiying Wang; Huiru (Jane) Zheng (Ed.) Proceedings of the 6th Collaborative European Research Conference (CERC 2020) Proceeding CEUR-WS.org, Aachen, Germany, vol. Vol. 2815, 2021, ISSN: 1613-0073, (urn:nbn:de:0074-2815-0). @proceedings{CERC2020,
title = {Proceedings of the 6th Collaborative European Research Conference (CERC 2020)},
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/, Proceedings on CEUR-WS},
issn = {1613-0073},
year = {2021},
date = {2021-02-17},
booktitle = {CERC2020 Proceedings},
volume = {Vol. 2815},
pages = {433},
publisher = {CEUR-WS.org},
address = {Aachen, Germany},
series = {CEUR Workshop Proceedings},
abstract = {In today's world, which has recently seen fractures and isolation forming among states, internationaland interdisciplinary collaboration is an increasingly important source of progress. Collaboration isa rich source of innovation and growth. It is the goal of the Collaborative European ResearchConference (CERC2020) to foster collaboration among friends and colleagues across disciplinesand nations within Europe. CERC emerged from long-standing cooperation between the CorkInstitute of Technology, Ireland and Hochschule Darmstadt - University of Applied Sciences,Germany. CERC has grown to include more well-established partners in Germany, the UnitedKingdom, Greece, Spain, Italy, and many more.
CERC is truly interdisciplinary, bringing together new and experienced researchers from science,engineering, business, humanities, and the arts. At CERC researchers not only present their findingsas published in their research papers. They are also challenged to collaboratively work out jointaspects of their research during conference sessions and informal social events and gatherings.
Organizing such an event involves the hard work of many people. COVID-19 pandemic hasimpacted our daily life and research. It has been a significant change to CERC2020 and this is thefirst time the conference was held virtually online. The conference has received submissions fromworldwide, not just European countries. Thanks go to the international program committee and myfellow program chairs, particularly to Prof Udo Bleimann for invaluable support throughout theconference. Prof Ingo Stengel, Dr. Haiying Wang, Dr. Ali Haithem, and Dr. Stefanie Regier forsupporting me in the review process. Dirk Burkhardt and Dr. Robert Loew put a great effort intosetting up the website and conference management system and preparing the conference programmeand proceedings. Thank my colleagues from Ulster University, Hochschule Karlsruhe andHochschule Darmstadt, and the Cork Institute of Technology, Ireland for providing invaluablesupport to the conference. CERC2020 has received supports from Ulster University, VIsit Belfast,and Belfast City Council.},
note = {urn:nbn:de:0074-2815-0},
keywords = {Art, Bioinformatics, Biology, Business Information Systems, Civil Engineering, Computer Science, Education, IT Security, Marketing, Multimedia, Psychology},
pubstate = {published},
tppubtype = {proceedings}
}
In today's world, which has recently seen fractures and isolation forming among states, internationaland interdisciplinary collaboration is an increasingly important source of progress. Collaboration isa rich source of innovation and growth. It is the goal of the Collaborative European ResearchConference (CERC2020) to foster collaboration among friends and colleagues across disciplinesand nations within Europe. CERC emerged from long-standing cooperation between the CorkInstitute of Technology, Ireland and Hochschule Darmstadt - University of Applied Sciences,Germany. CERC has grown to include more well-established partners in Germany, the UnitedKingdom, Greece, Spain, Italy, and many more.
CERC is truly interdisciplinary, bringing together new and experienced researchers from science,engineering, business, humanities, and the arts. At CERC researchers not only present their findingsas published in their research papers. They are also challenged to collaboratively work out jointaspects of their research during conference sessions and informal social events and gatherings.
Organizing such an event involves the hard work of many people. COVID-19 pandemic hasimpacted our daily life and research. It has been a significant change to CERC2020 and this is thefirst time the conference was held virtually online. The conference has received submissions fromworldwide, not just European countries. Thanks go to the international program committee and myfellow program chairs, particularly to Prof Udo Bleimann for invaluable support throughout theconference. Prof Ingo Stengel, Dr. Haiying Wang, Dr. Ali Haithem, and Dr. Stefanie Regier forsupporting me in the review process. Dirk Burkhardt and Dr. Robert Loew put a great effort intosetting up the website and conference management system and preparing the conference programmeand proceedings. Thank my colleagues from Ulster University, Hochschule Karlsruhe andHochschule Darmstadt, and the Cork Institute of Technology, Ireland for providing invaluablesupport to the conference. CERC2020 has received supports from Ulster University, VIsit Belfast,and Belfast City Council. |
| 115. | Lukas Kaupp; Heiko Webert; Kawa Nazemi; Bernhard Humm; Stephan Simons CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory Inproceedings In: Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020), pp. 492-501, Elsevier, 2021, ISSN: 1877-0509. @inproceedings{Kaupp2021,
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},
doi = {10.1016/j.procs.2021.01.265},
issn = {1877-0509},
year = {2021},
date = {2021-02-17},
booktitle = {Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)},
volume = {180},
pages = {492-501},
publisher = {Elsevier},
series = {Procedia Computer Science},
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.},
keywords = {anomaly detection, contextual faults, cyber-physical systems, fault diagnosis, smart factory},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 114. | Kawa Nazemi; Lukas Kaupp; Dirk Burkhardt; Nicola Below Datenvisualisierung Book Chapter In: Markus Putnings; Heike Neuroth; Janna Neumann (Ed.): Praxishandbuch Forschungsdatenmanagement, Chapter 5.4, pp. 477-502, De Gruyter, Berlin/Boston, 2021, ISBN: 978-3-11-065365-6. @inbook{Nazemi2021,
title = {Datenvisualisierung},
author = {Kawa Nazemi and Lukas Kaupp and Dirk Burkhardt and Nicola Below},
editor = {Markus Putnings and Heike Neuroth and Janna Neumann},
doi = {10.1515/9783110657807-026},
isbn = {978-3-11-065365-6},
year = {2021},
date = {2021-01-18},
booktitle = {Praxishandbuch Forschungsdatenmanagement},
pages = {477-502},
publisher = {De Gruyter},
address = {Berlin/Boston},
chapter = {5.4},
abstract = {Die visuelle Projektion von heterogenen (z. B. Forschungs-)Daten auf einer 2-dimensionalen Fläche, wie etwa einem Bildschirm, wird als Datenvisualisierung bezeichnet. Datenvisualisierung ist ein Oberbegriff für verschiedene Arten der visuellen Projektion. In diesem Kapitel wird zunächst der Begriff definiert und abgegrenzt. Der Fokus des Kapitels liegt auf Informationsvisualisierung und Visual Analytics. In diesem Kontext wird der Prozess der visuellen Transformation vorgestellt. Es soll als Grundlage für eine wissenschaftlich valide Generierung von Visualisierungen dienen, die auch visuelle Aufgaben umfassen. Anwendungsszenarien stellen den Mehrwert der hier vorgestellten Konzepte in der Praxis vor. Der wissenschaftliche Beitrag liegt in einer formalen Definition des visuellen Mappings.},
keywords = {Data Visualization},
pubstate = {published},
tppubtype = {inbook}
}
Die visuelle Projektion von heterogenen (z. B. Forschungs-)Daten auf einer 2-dimensionalen Fläche, wie etwa einem Bildschirm, wird als Datenvisualisierung bezeichnet. Datenvisualisierung ist ein Oberbegriff für verschiedene Arten der visuellen Projektion. In diesem Kapitel wird zunächst der Begriff definiert und abgegrenzt. Der Fokus des Kapitels liegt auf Informationsvisualisierung und Visual Analytics. In diesem Kontext wird der Prozess der visuellen Transformation vorgestellt. Es soll als Grundlage für eine wissenschaftlich valide Generierung von Visualisierungen dienen, die auch visuelle Aufgaben umfassen. Anwendungsszenarien stellen den Mehrwert der hier vorgestellten Konzepte in der Praxis vor. Der wissenschaftliche Beitrag liegt in einer formalen Definition des visuellen Mappings. |
| 113. | Mina Schütz Detection and Identification of Fake News: Binary Content Classification with Pre-trained Language Models Incollection In: Information between Data and Knowledge, vol. 74, pp. 422–431, Werner Hülsbusch, Glückstadt, 2021, (Gerhard Lustig Award Papers). @incollection{Sc21,
title = {Detection and Identification of Fake News: Binary Content Classification with Pre-trained Language Models},
author = {Mina Schütz},
year = {2021},
date = {2021-01-01},
booktitle = {Information between Data and Knowledge},
volume = {74},
pages = {422--431},
publisher = {Werner Hülsbusch},
address = {Glückstadt},
series = {Schriften zur Informationswissenschaft},
abstract = {Fake news has emerged as a critical problem for society and professional journalism. Many individuals consume their news via online media, such as social networks and news websites. Therefore, the demand for automatic fake news detection is increasing. There is still no agreed upon definition for fake news, since it can include various concepts, such as clickbait, propaganda, satire, hoaxes, and rumors. This results in a broad landscape of machine learning approaches, which have a varying accuracy in detecting fake news. This masterthesis focused on a binary content-based classification approach, with a bidirectional Transformer ( BERT ), to detect fake news in online articles. BERT creates a pretrained language model during training and is fine-tuned on a labeled dataset. The FakeNewsNet dataset is used to test two variants of the model (cased / uncased) with articles, using only the body text, the title, and a concatenation of both. Additionally, both models were tested with different preprocessing steps. The models gain in all 29 carried out experiments high accuracy results, without overfitting. Using the body text and the concatenation resulted in five models with an accuracy of 87% after testing, whereas using only titles resulted in 84%. This shows that short statements could be already enough for fake news detection using language models. Also, the preprocessing steps seem to have no major impact on the predictions. It is concluded that transformer models, such as BERT , are a promising approach to detect fake news, since it achieves notable results, even without using a large dataset.},
note = {Gerhard Lustig Award Papers},
keywords = {fake news; fake news detection; BERT; transformer; pre-trained language model; binary classification},
pubstate = {published},
tppubtype = {incollection}
}
Fake news has emerged as a critical problem for society and professional journalism. Many individuals consume their news via online media, such as social networks and news websites. Therefore, the demand for automatic fake news detection is increasing. There is still no agreed upon definition for fake news, since it can include various concepts, such as clickbait, propaganda, satire, hoaxes, and rumors. This results in a broad landscape of machine learning approaches, which have a varying accuracy in detecting fake news. This masterthesis focused on a binary content-based classification approach, with a bidirectional Transformer ( BERT ), to detect fake news in online articles. BERT creates a pretrained language model during training and is fine-tuned on a labeled dataset. The FakeNewsNet dataset is used to test two variants of the model (cased / uncased) with articles, using only the body text, the title, and a concatenation of both. Additionally, both models were tested with different preprocessing steps. The models gain in all 29 carried out experiments high accuracy results, without overfitting. Using the body text and the concatenation resulted in five models with an accuracy of 87% after testing, whereas using only titles resulted in 84%. This shows that short statements could be already enough for fake news detection using language models. Also, the preprocessing steps seem to have no major impact on the predictions. It is concluded that transformer models, such as BERT , are a promising approach to detect fake news, since it achieves notable results, even without using a large dataset. |
| 112. | Mina Schütz; Alexander Schindler; Melanie Siegel; Kawa Nazemi Automatic Fake News Detection with Pre-Trained Transformer Models Inproceedings In: Del Bimbo; al (Ed.): Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Sciences, Springer, Cham, 2021. @inproceedings{SSSN21,
title = {Automatic Fake News Detection with Pre-Trained Transformer Models},
author = {Mina Schütz and Alexander Schindler and Melanie Siegel and Kawa Nazemi},
editor = {Del Bimbo and al},
year = {2021},
date = {2021-01-01},
booktitle = {Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Sciences},
volume = {12667},
publisher = {Springer},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 111. | Mina Schütz; Alexander Schindler; Melanie Siegel Disinformation Detection: An Explainable Transfer Learning Approach Miscellaneous 2021. @misc{SSS21b,
title = {Disinformation Detection: An Explainable Transfer Learning Approach},
author = {Mina Schütz and Alexander Schindler and Melanie Siegel},
url = {https://www.unibw.de/code-events/code2021/04_schuetz.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {CODE2021},
address = {Munich, Germany},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
| 110. | Mina Schütz; Jaqueline Böck; Daria Liakhovets; Djordje Slijepčević; Armin Kirchknopf; Manuel Hecht; Johannes Bogensperger; Sven Schlarb; Alexander Schindler; Matthias Zeppelzauer Automatic Sexism Detection with Multilingual Transformer Models Inproceedings In: Manuel Montes; Paolo Rosso; Julio Gonzalo; Mario Ezra Aragón; Rodrigo Agerri; Miguel Ángel Álvarez-Carmona; Elena Álvarez Mellado; Jorge Carrillo-de-Albornoz; Luis Chiruzzo; Larissa Freitas; Helena Gómez Adorno; Yoan Gutiérrez; Salud María Jiménez Zafra; Salvador Lima-López; Flor Miriam Plaza-de-Arco; Mariona Taulé (Ed.): Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2021), pp. 346-355, Málaga, Spain, 2021. @inproceedings{SBLS21,
title = {Automatic Sexism Detection with Multilingual Transformer Models},
author = {Mina Schütz and Jaqueline Böck and Daria Liakhovets and Djordje Slijepčević and Armin Kirchknopf and Manuel Hecht and Johannes Bogensperger and Sven Schlarb and Alexander Schindler and Matthias Zeppelzauer},
editor = {Manuel Montes and Paolo Rosso and Julio Gonzalo and Mario Ezra Aragón and Rodrigo Agerri and Miguel Ángel Álvarez-Carmona and Elena Álvarez Mellado and Jorge Carrillo-de-Albornoz and Luis Chiruzzo and Larissa Freitas and Helena Gómez Adorno and Yoan Gutiérrez and Salud María Jiménez Zafra and Salvador Lima-López and Flor Miriam Plaza-de-Arco and Mariona Taulé},
url = {https://ceur-ws.org/Vol-2943/exist_paper1.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2021)},
pages = {346-355},
address = {Málaga, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 109. | Midhad Blazevic; Lennart B. Sina; Dirk Burkhardt; Melanie Siegel; Kawa Nazemi 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. @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},
doi = {10.1109/IV53921.2021.00041},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
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, Collaborative Systems, Information visualization, Similarity, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
2020 |
| 108. | Dirk Burkhardt; Kawa Nazemi; Egils Ginters Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics Inproceedings In: Janis Grabis; Andrejs Romanovs; Galina Kulesova (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. @inproceedings{Burkhardt2020c,
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 = {2020-11-19},
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 = {Adaptive Visualization, logistics, Process Mining, Transportation, Trend Analytics, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 107. | Artis Aizstrauts; Dirk Burkhardt; Egils Ginters; Kawa Nazemi On Microservice Architecture Based Communication Environment for Cycling Map Developing and Maintenance Simulator Inproceedings In: Janis Grabis; Andrejs Romanovs; Galina Kulesova (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. @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 = {2020-11-19},
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 = {Easy Communication Environment, microservice architecture, Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 106. | Kawa Nazemi; Matthias Kowald; Till Dannewald; Dirk Burkhardt; Egils Ginters Visual Analytics Indicators for Mobility and Transportation Inproceedings In: Janis Grabis; Andrejs Romanovs; Galina Kulesova (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. @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 = {2020-11-19},
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 = {mobility analytics, mobility behaviour, mobility indicators for visual analytics, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 105. | Lennart B. Sina; Dirk Burkhardt; Kawa Nazemi Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts Inproceedings In: Haithem Afli; Udo Bleimann; Dirk Burkhardt; Robert Loew; Stefanie Regier; Ingo Stengel; Haiying Wang; Huiru (Jane) Zheng (Ed.): Proceedings of the 6th Collaborative European Research Conference (CERC 2020), pp. 222-235, CEUR-WS.org, Aachen, Germany, 2020, ISSN: 1613-0073, (urn:nbn:de:0074-2815-0). @inproceedings{Sina2021,
title = {Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts},
author = {Lennart B. 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},
issn = {1613-0073},
year = {2020},
date = {2020-09-11},
urldate = {2020-09-11},
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 = {business intelligence, information exploration, Innovation Management, Visual analytics, Visual Trend Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 104. | Kawa Nazemi; Maike J. Klepsch; Dirk Burkhardt; Lukas Kaupp 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, New York, USA, 2020, ISBN: 978-1-7281-9134-8. @inproceedings{Nazemi2020d,
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},
isbn = {978-1-7281-9134-8},
year = {2020},
date = {2020-09-01},
urldate = {2020-09-01},
booktitle = {2020 24th International Conference Information Visualisation (IV)},
pages = {360-367},
publisher = {IEEE},
address = {New York, USA},
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 = {Data Science, Natural Language Processing, Visual analytics, Visual Trend Analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 103. | Lukas Kaupp; Kawa Nazemi; Bernhard Humm 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, New York, USA, 2020, ISBN: 978-1-7281-9134-8. @inproceedings{Nazemi2020db,
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},
isbn = {978-1-7281-9134-8},
year = {2020},
date = {2020-09-01},
booktitle = {2020 24th International Conference Information Visualisation (IV)},
pages = {350-359},
publisher = {IEEE},
address = {New York, USA},
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 = {Analytical models, cyber-physical systems, Data Science, Industries, Outlier Detection, Pipelines, Protocols, Reasoning, Smart manufacturing, Task analysis, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 102. | Ebad Banissi; Farzad Khosrow-shahi; Anna Ursyn; Mark W. McK. Bannatyne; João Moura Pires; Nuno Datia; Kawa Nazemi; Boris Kovalerchuk; John Counsell; Andrew Agapiou; Zora Vrcelj; Hing-Wah Chau; Mengbi Li; Gehan Nagy; Richard Laing; Rita Francese; Muhammad Sarfraz; Fatma Bouali; Gilles Venturin; Marjan Trutschl; Urska Cvek; Heimo Müller; Minoru Nakayama; Marco Temperini; Tania Di Mascio; Filippo SciarroneVeronica Rossano; Ralf Dörner; Loredana Caruccio; Autilia Vitiello; Weidong Huang; Michele Risi; Ugo Erra; Razvan Andonie; Muhammad Aurangzeb Ahmad; Ana Figueiras; and Mabule Samuel Mabakane (Ed.) Proceedings of 2020 24th International Conference Information Visualisation (IV) Proceeding IEEE, New York, USA, 2020, ISBN: 978-1-7281-9134-8. @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 and Mabule Samuel Mabakane},
doi = {10.1109/IV51561.2020},
isbn = {978-1-7281-9134-8},
year = {2020},
date = {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 = {Information visualization},
pubstate = {published},
tppubtype = {proceedings}
}
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. |
| 101. | Artis Aizstrauts; Egils Ginters; Dirk Burkhardt; Kawa Nazemi Bicycle Path Network Designing and Exploitation Simulation as a Microservice Architecture Inproceedings In: Egils Ginters; Mario Arturo Ruiz Estrada; Miquel Angel Piera Eroles (Ed.): ICTE in Transportation and Logistics 2019, pp. 344–351, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-39688-6. @inproceedings{Aizstrauts2020,
title = {Bicycle Path Network Designing and Exploitation Simulation as a Microservice Architecture},
author = {Artis Aizstrauts and Egils Ginters and Dirk Burkhardt and Kawa Nazemi},
editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},
doi = {10.1007/978-3-030-39688-6_43},
isbn = {978-3-030-39688-6},
year = {2020},
date = {2020-01-31},
booktitle = {ICTE in Transportation and Logistics 2019},
pages = {344--351},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Simulation is recognized as a suitable tool for sociotechnical systems research. But the variety and complexity of sociotechnical systems often leads to the need for distributed simulation solutions to understand them. Models that are built for infrastructure planning are typical examples. They combine different domains and involve variety of simulation approaches. This article proposes an easy management environment that is used for VeloRouter software -- a multi agent-based bicycle path network and exploitation simulator that is built as a microservice architecture where each domain simulation is executed as a different microservice.},
keywords = {Bicycle path network planning, Easy Communication Environment, Sociotechnical systems simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Simulation is recognized as a suitable tool for sociotechnical systems research. But the variety and complexity of sociotechnical systems often leads to the need for distributed simulation solutions to understand them. Models that are built for infrastructure planning are typical examples. They combine different domains and involve variety of simulation approaches. This article proposes an easy management environment that is used for VeloRouter software -- a multi agent-based bicycle path network and exploitation simulator that is built as a microservice architecture where each domain simulation is executed as a different microservice. |
| 100. | Kawa Nazemi; Dirk Burkhardt; Lukas Kaupp; Till Dannewald; Matthias Kowald; Egils Ginters Visual Analytics in Mobility, Transportation and Logistics Inproceedings In: Egils Ginters; Mario Arturo Ruiz Estrada; Miquel Angel Piera Eroles (Ed.): ICTE in Transportation and Logistics 2019, pp. 82–89, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-39688-6. @inproceedings{Nazemi2020,
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},
doi = {10.1007/978-3-030-39688-6_12},
isbn = {978-3-030-39688-6},
year = {2020},
date = {2020-01-01},
booktitle = {ICTE in Transportation and Logistics 2019},
pages = {82--89},
publisher = {Springer International Publishing},
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 = {Data Analytics, Mobility Behavior, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
| 99. | Dirk Burkhardt; Kawa Nazemi; Egils Ginters Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations Inproceedings In: Egils Ginters; Mario Arturo Ruiz Estrada; Miquel Angel Piera Eroles (Ed.): ICTE in Transportation and Logistics 2019, pp. 319–327, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-39688-6. @inproceedings{Burkhardt2020,
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},
doi = {10.1007/978-3-030-39688-6_40},
isbn = {978-3-030-39688-6},
year = {2020},
date = {2020-01-01},
booktitle = {ICTE in Transportation and Logistics 2019},
pages = {319--327},
publisher = {Springer International Publishing},
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 = {Adaptive Visualization, Process Mining, Transportation and Logistics},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
