Publikationen
Nazemi, Kawa Artificial Intelligence in Visual Analytics Proceedings Article In: Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award, S. 230 - 237, IEEE CPS, 2023. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual tasks Sina, Lennart B.; Secco, Cristian A.; Blazevic, Midhad; Nazemi, Kawa Visual Analytics for Corporate Foresight - A Conceptual Approach Proceedings Article In: Proceedings of the 27th International Conference Information Visualisation (IV2023), S. 244-250, IEEE CPS, 2023. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Visual analytics Banissi, Ebad; Siirtola, Harri; Ursyn, Anna; Pires, João Moura; Datia, Nuno; Nazemi, Kawa; Kovalerchuk, Boris; Andonie, Razvan; Nakayama, Minoru; Temperini, Marco; Sciarrone, Filippo; Nguyen, Quang Vinh; Mabakane, Mabule Samuel; Rusu, Adrian; Cvek, Urska; Trutschl, Marjan; Mueller, Heimo; Francese, Rita; Boua-li, Fatma; Venturini, Gilles (Hrsg.) Proceedings of 2023 27th International Conference Information Visualisation Konferenzberichte 2023, ISBN: 979-8-3503-4161-4. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard In: Electronics, Bd. 11, Nr. 23, 2022, ISSN: 2079-9292. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Buchkapitel In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Hrsg.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, S. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model Buchkapitel In: Kovalerchuk, Boris; Nazemi, Kawa; Andonie, Răzvan; Datia, Nuno; Banissi, Ebad (Hrsg.): Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery, S. 403–436, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93119-3. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery Nazemi, Kawa; Burkhardt, Dirk; Kock, Alexander In: Multimedia Tools and Applications, Bd. 1198, 2021, ISSN: 1573-7721, (Springer Nature). Abstract | Links | BibTeX | Schlagwörter: Emerging Trend Identification, Information visualization, Innovation Management, Interaction Design, Multimedia Interaction, Technology Management, Visual analytics, Visual Trend Analytics Blazevic, Midhad; Sina, Lennart B.; Burkhardt, Dirk; Siegel, Melanie; Nazemi, Kawa Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Proceedings Article In: 2021 25th International Conference Information Visualisation (IV), S. 211-217, IEEE , 2021. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics Sina, Lennart; Burkhardt, Dirk; Nazemi, Kawa Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts Proceedings Article In: Afli, Haithem; Bleimann, Udo; Burkhardt, Dirk; Loew, Robert; Regier, Stefanie; Stengel, Ingo; Wang, Haiying; Zheng, Huiru (Jane) (Hrsg.): Proceedings of the 6th Collaborative European Research Conference (CERC 2020), S. 222-235, CEUR-WS.org, Aachen, Germany, 2021, ISSN: 1613-0073, (urn:nbn:de:0074-2815-0). Abstract | Links | BibTeX | Schlagwörter: Business intelligence, information exploration, Innovation Management, Visual analytics, Visual Trend Analysis Nazemi, Kawa; Kowald, Matthias; Dannewald, Till; Burkhardt, Dirk; Ginters, Egils Visual Analytics Indicators for Mobility and Transportation Proceedings Article In: Grabis, Janis; Romanovs, Andrejs; Kulesova, Galina (Hrsg.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), S. 1-6, IEEE, 2020, ISBN: 978-1-7281-9105-8. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Mobility, Visual analytics Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics Proceedings Article In: Grabis, Janis; Romanovs, Andrejs; Kulesova, Galina (Hrsg.): 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), S. 1-6, IEEE, 2020, ISBN: 978-1-7281-9105-8. Abstract | Links | BibTeX | Schlagwörter: Adaptive visualization, logistics, Process Mining, Transportation, Trend Analytics, Visual analytics Kaupp, Lukas; Nazemi, Kawa; Humm, Bernhard An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing Proceedings Article In: 2020 24th International Conference Information Visualisation (IV), S. 350-359, IEEE Computer Society, 2020, ISSN: 2375-0138. Abstract | Links | BibTeX | Schlagwörter: Analytical models, cyber-physical systems, Data Science, Industries, Outlier Detection, Pipelines;Task analysis, Protocols, Reasoning, Smart manufacturing, Visual analytics Nazemi, Kawa; Klepsch, Maike J.; Burkhardt, Dirk; Kaupp, Lukas Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing Proceedings Article In: 2020 24th International Conference Information Visualisation (IV), S. 360-367, IEEE Computer Society, 2020, ISSN: 2375-0138. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Data Science, Information Science, Information visualization, Large scale integration, Libraries, Machine Leanring, Market Research, Natural Language Processing, Visual analytics, Visual Trend Analytics Nazemi, Kawa; Burkhardt, Dirk; Kaupp, Lukas; Dannewald, Till; Kowald, Matthias; Ginters, Egils Visual Analytics in Mobility, Transportation and Logistics Proceedings Article In: Ginters, Egils; Estrada, Mario Arturo Ruiz; Eroles, Miquel Angel Piera (Hrsg.): ICTE in Transportation and Logistics 2019, S. 82–89, Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure, Cham, 2020, ISBN: 978-3-030-39688-6. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Mobility, Visual analytics Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations Proceedings Article In: Ginters, Egils; Estrada, Mario Arturo Ruiz; Eroles, Miquel Angel Piera (Hrsg.): ICTE in Transportation and Logistics 2019, S. 319–327, Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure, Cham, 2020, ISBN: 978-3-030-39688-6. Abstract | Links | BibTeX | Schlagwörter: 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 Nazemi, Kawa; Burkhardt, Dirk Advanced Visual Analytical Reasoning for Technology and Innovation Management (AVARTIM) Sonstige Forschungstag 2019 der Hessischen Hochschulen für Angewandte Wissenschaften (HAW), Frankfurt, Germany, 2019. Abstract | Links | BibTeX | Schlagwörter: Innovation Management, Technology Management, Trend Analytics, Visual Analytical Reasoning, Visual analytics Nazemi, Kawa; Burkhardt, Dirk A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Ushizima, Daniela; Chai, Sek; Sueda, Shinjiro; Lin, Xin; Lu, Aidong; Thalmann, Daniel; Wang, Chaoli; Xu, Panpan (Hrsg.): Advances in Visual Computing, S. 283–294, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-33723-0. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Machine Leanring, Visual analytics Nazemi, K; Burkhardt, D Visual Analytics for Analyzing Technological Trends from Text Proceedings Article In: 2019 23rd International Conference Information Visualisation (IV), S. 191-200, 2019, ISSN: 2375-0138, (Best Paper Award). Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Information visualization, Machine Leanring, Market research;Visualization;Data mining;Data visualization;Data models;Hidden Markov models;Patents;Visual Analytics;information visualization;trend analytics;emerging trend identification;visual business analytics, Visual analytics Nazemi, Kawa Visual Trend Analytics in Digital Libraries Sonstige Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval., 2019. Abstract | Links | BibTeX | Schlagwörter: Information visualization, Trend analysis, Trend Analytics, Visual analytics Nazemi, Kawa; Burkhardt, Dirk The 4th International Conference of the Virtual and Augmented Reality in Education, I3M I3M, 2018, ISBN: 978-88-85741-21-8. Abstract | Links | BibTeX | Schlagwörter: Information visualization, User-centered design, Visual analytics2023
@inproceedings{Nazemi2023,
title = {Artificial Intelligence in Visual Analytics},
author = {Kawa Nazemi},
doi = {10.1109/IV60283.2023.00048},
year = {2023},
date = {2023-11-30},
urldate = {2023-11-30},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award},
journal = {Proceedings of the 27th International Conference Information Visualisation (IV2023) - Best Paper Award-},
pages = {230 - 237},
publisher = {IEEE CPS},
abstract = {Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.},
keywords = {Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual tasks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{SinaIV2023,
title = {Visual Analytics for Corporate Foresight - A Conceptual Approach},
author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},
doi = {10.1109/IV60283.2023.00050},
year = {2023},
date = {2023-11-29},
urldate = {2023-11-29},
booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)},
pages = {244-250},
publisher = {IEEE CPS},
abstract = {Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data.},
keywords = {Artificial Intelligence, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@proceedings{Banissi2023,
title = {Proceedings of 2023 27th International Conference Information Visualisation},
editor = {Ebad Banissi and Harri Siirtola and Anna Ursyn and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and Razvan Andonie and Minoru Nakayama and Marco Temperini and Filippo Sciarrone and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Urska Cvek and Marjan Trutschl and Heimo Mueller and Rita Francese and Fatma Boua-li and Gilles Venturini},
doi = {10.1109/IV60283.2023.00001},
isbn = {979-8-3503-4161-4},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
issue = {IV2023},
abstract = {Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt.},
keywords = {Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {proceedings}
}
2022
@article{electronics11233942,
title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Kawa Nazemi and Egils Ginters and Michael Bazant},
url = {https://www.mdpi.com/2079-9292/11/23/3942},
doi = {10.3390/electronics11233942},
issn = {2079-9292},
year = {2022},
date = {2022-11-01},
urldate = {2022-01-01},
journal = {Electronics},
volume = {11},
number = {23},
abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.},
keywords = {Artificial Intelligence, Case study, Data Analytics, Data Science, Data visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
@inbook{Kaupp2022,
title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
url = {https://doi.org/10.1007/978-3-030-93119-3_16},
doi = {10.1007/978-3-030-93119-3_16},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {403--436},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},
keywords = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{Kaupp2022b,
title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},
author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},
editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},
doi = {10.1007/978-3-030-93119-3_16},
isbn = {978-3-030-93119-3},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},
pages = {403–436},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},
keywords = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},
pubstate = {published},
tppubtype = {inbook}
}
2021
@article{Nazemi2021,
title = {Visual analytics for Technology and Innovation Management: An interaction approach for strategic decisionmaking},
author = {Kawa Nazemi and Dirk Burkhardt and Alexander Kock},
url = {https://link.springer.com/content/pdf/10.1007/s11042-021-10972-3.pdf, Open Access PDF},
doi = {10.1007/s11042-021-10972-3},
issn = {1573-7721},
year = {2021},
date = {2021-05-20},
urldate = {2021-05-20},
journal = {Multimedia Tools and Applications},
volume = {1198},
abstract = {The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.},
note = {Springer Nature},
keywords = {Emerging Trend Identification, Information visualization, Innovation Management, Interaction Design, Multimedia Interaction, Technology Management, Visual analytics, Visual Trend Analytics},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9582711,
title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data},
author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi},
url = {https://ieeexplore.ieee.org/document/9582711, IEEE Xplore},
doi = {10.1109/IV53921.2021.00041},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
booktitle = {2021 25th International Conference Information Visualisation (IV)},
pages = {211-217},
publisher = {IEEE },
abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.},
keywords = {Artificial Intelligence, Collaboration, Information visualization, Similarity, Visual analytics},
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}
}
2020
@inproceedings{Nazemi2020c,
title = {Visual Analytics Indicators for Mobility and Transportation},
author = {Kawa Nazemi and Matthias Kowald and Till Dannewald and Dirk Burkhardt and Egils Ginters},
editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},
doi = {10.1109/ITMS51158.2020.9259321},
isbn = {978-1-7281-9105-8},
year = {2020},
date = {2020-09-10},
booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},
pages = {1-6},
publisher = {IEEE},
abstract = {Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.},
keywords = {Artificial Intelligence, Mobility, Visual analytics},
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}
}
2019
@misc{Nazemi2019db,
title = {Advanced Visual Analytical Reasoning for Technology and Innovation Management (AVARTIM)},
author = {Kawa Nazemi and Dirk Burkhardt},
url = {https://www.hessen.de/presse/veranstaltung/forschungstag-2019-der-hessischen-hochschulen-fuer-angewandte-wissenschaften, Event Website},
doi = {10.5281/zenodo.3517296},
year = {2019},
date = {2019-10-29},
abstract = {Im Rahmen des Vorhabens soll mit „AVARTIM“ ein softwaregestützter Prozess zum Erkennen und Bewerten von Trends, Markt- und Technologiesignalen entwickelt werden, um den Prozess des Innovations- und Technologiemanagements nachhaltig zu unterstützen. Dabei soll im Rahmen des Vorhabens eine Infrastruktur an der Hochschule Darmstadt aufgebaut werden, die modular ist und somit auf technologische Veränderungen schnell reagieren kann. Die zu entwickelnde Infrastruktur dient hierbei als Vorlaufforschung und Ausgangstechnologie sowohl für den industriellen Einsatz durch und mit den KMU Partnern als auch zur Beantragung von Verbundvorhaben.},
howpublished = {Forschungstag 2019 der Hessischen Hochschulen für Angewandte Wissenschaften (HAW), Frankfurt, Germany},
keywords = {Innovation Management, Technology Management, Trend Analytics, Visual Analytical Reasoning, Visual analytics},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{Nazemi_ISVC2019,
title = {A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management},
author = {Kawa Nazemi and Dirk Burkhardt},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu},
url = {https://rd.springer.com/chapter/10.1007/978-3-030-33723-0_23, Springer LNCS},
doi = {10.1007/978-3-030-33723-0_23},
isbn = {978-3-030-33723-0},
year = {2019},
date = {2019-10-09},
booktitle = {Advances in Visual Computing},
pages = {283--294},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.},
keywords = {Artificial Intelligence, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Machine Leanring, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi-IV2019,
title = {Visual Analytics for Analyzing Technological Trends from Text},
author = {K Nazemi and D Burkhardt},
doi = {10.1109/IV.2019.00041},
issn = {2375-0138},
year = {2019},
date = {2019-07-01},
booktitle = {2019 23rd International Conference Information Visualisation (IV)},
pages = {191-200},
abstract = {The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends.},
note = {Best Paper Award},
keywords = {Artificial Intelligence, Information visualization, Machine Leanring, Market research;Visualization;Data mining;Data visualization;Data models;Hidden Markov models;Patents;Visual Analytics;information visualization;trend analytics;emerging trend identification;visual business analytics, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{Naz19ASIST,
title = {Visual Trend Analytics in Digital Libraries},
author = {Kawa Nazemi},
url = {https://zenodo.org/record/3264801#.XSBcMo_gpaR, Zenodo Open Access},
doi = {10.5281/zenodo.3264801},
year = {2019},
date = {2019-04-26},
abstract = {The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies’ reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3].},
howpublished = {Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval.},
keywords = {Information visualization, Trend analysis, Trend Analytics, Visual analytics},
pubstate = {published},
tppubtype = {misc}
}
2018
@conference{Nazemi2018b,
title = {Juxtaposing Visual Layouts – An Approach for Solving Analytical and Exploratory Tasks through Arranging Visual Interfaces},
author = {Kawa Nazemi and Dirk Burkhardt},
editor = {A. G. Bruzzone and E. GINTERS and E. G. Mendívil and J. M. Guitierrez and F. Longo},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85056741373&origin=inward&txGid=9b80a3dc76c1623f440ddf04fde00bea, Scopus},
doi = {10.5281/zenodo.2542952},
isbn = {978-88-85741-21-8},
year = {2018},
date = {2018-09-18},
booktitle = {The 4th International Conference of the Virtual and Augmented Reality in Education},
publisher = {I3M},
organization = {I3M},
abstract = {Interactive visualization and visual analytics systems enables solving a variety of tasks. Starting with simple search tasks for outliers, anomalies etc. in data to analytical comparisons, information visualizations may lead to a faster and more precise solving of tasks. There exist a variety of methods to support users in the process of task solving, e.g. superimposing, juxtaposing or partitioning complex visual structures. Commonly all these methods make use of a single data source that is visualized at the same time. We propose in this paper an approach that goes beyond the established methods and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual interfaces to enable solving complex tasks.},
keywords = {Information visualization, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {conference}
}