@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}

}

@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}

}

@inproceedings{Sina2021,

title = {Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts},

author = {Lennart Sina and Dirk Burkhardt and Kawa Nazemi},

editor = {Haithem Afli and Udo Bleimann and Dirk Burkhardt and Robert Loew and Stefanie Regier and Ingo Stengel and Haiying Wang and Huiru (Jane) Zheng},

url = {http://ceur-ws.org/Vol-2815/CERC2020_paper14.pdf, Paper on CEUR-WS, Full PDF},

issn = {1613-0073},

year  = {2021},

date = {2021-02-17},

booktitle = {Proceedings of the 6th Collaborative European Research Conference (CERC 2020)},

volume = {Vol. 2815},

pages = {222-235},

publisher = {CEUR-WS.org},

address = {Aachen, Germany},

series = {CEUR Workshop Proceedings},

abstract = {The rapid change due to digitalization challenge a variety of market players and force them to find strategies to be aware of developments in these markets, particularly those that impact their business. The main challenge is what a practical solution could look like and how technology can support market players in these trend observation tasks. The paper outlines therefore a technological solution to observe specific authors e.g. researchers who influence a certain market or engineers of competitors. In many branches both are well-known groups to market players and there is almost always the need of a technology that supports the topical observation. This paper focuses on the concept of how a visual dashboard could enable a market observation and how data must be processed for it and its prototypical implementation which enables an evaluation later. Furthermore, the definition of a principal technological analysis for innovation and technology management is created and is also an important contribution to the scientific community that specifically considers the technology perspective and its corresponding requirements.},

note = {urn:nbn:de:0074-2815-0},

keywords = {Business intelligence, information exploration, Innovation Management, Visual analytics, Visual Trend Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{Aizstrauts2020c,

title = {On Microservice Architecture Based Communication Environment for Cycling Map Developing and Maintenance Simulator},

author = {Artis Aizstrauts and Dirk Burkhardt and Egils Ginters and Kawa Nazemi},

editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},

doi = {10.1109/ITMS51158.2020.9259299},

isbn = {978-1-7281-9105-8},

year  = {2020},

date = {2020-09-09},

booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},

pages = {1-4},

publisher = {IEEE},

abstract = {Urban transport infrastructure nowadays involves environmentally friendly modes of transport, the most democratic of which is cycling. Citizens will use bicycles if a reasonably designed cycle path scheme will be provided. Cyclists also need to know the characteristics and load of the planned route before the trip. Prediction can be provided by simulation, but it is often necessary to use heterogeneous and distributed models that require a specific communication environment to ensure interaction. The article describes the easy communication environment that is used to provide microservices communication and data exchange in a bicycle route design and maintenance multi-level simulator.},

keywords = {Easy Communication Environment, microservice architecture, Simulation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2020cb,

title = {Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},

editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},

doi = {10.1109/ITMS51158.2020.9259309},

isbn = {978-1-7281-9105-8},

year  = {2020},

date = {2020-09-09},

booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},

pages = {1-6},

publisher = {IEEE},

abstract = {A variety of new technologies and ideas for businesses are arising in the domain of logistics and mobility. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users’ behavior is commonly investigated in adaptive systems, which is considering the induvial preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows generating a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks.},

keywords = {Adaptive visualization, logistics, Process Mining, Transportation, Trend Analytics, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kaupp_IV2020,

title = {An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing},

author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},

doi = {10.1109/IV51561.2020.00064},

issn = {2375-0138},

year  = {2020},

date = {2020-09-01},

booktitle = {2020 24th International Conference Information Visualisation (IV)},

pages = {350-359},

publisher = {IEEE Computer Society},

abstract = {The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.},

keywords = {Analytical models, cyber-physical systems, Data Science, Industries, Outlier Detection, Pipelines;Task analysis, Protocols, Reasoning, Smart manufacturing, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi_IV2020,

title = {Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing},

author = {Kawa Nazemi and Maike J. Klepsch and Dirk Burkhardt and Lukas Kaupp},

doi = {10.1109/IV51561.2020.00065},

issn = {2375-0138},

year  = {2020},

date = {2020-09-01},

booktitle = {2020 24th International Conference Information Visualisation (IV)},

pages = {360-367},

publisher = {IEEE Computer Society},

abstract = {Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes.},

keywords = {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}

}

@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}

}

@inproceedings{10.1007/978-3-030-39688-6_40,

title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},

editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},

url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_40, Springer },

doi = {10.1007/978-3-030-39688-6_40},

isbn = {978-3-030-39688-6},

year  = {2020},

date = {2020-01-30},

booktitle = {ICTE in Transportation and Logistics 2019},

pages = {319--327},

publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},

address = {Cham},

abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.},

keywords = {Human Factors, Human-computer interaction (HCI), Mobility, personalization, Process Support, Process-Mining, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}